def fingerprint(logcan, sendcan, has_relay):
params = Params()
car_params = params.get("CarParams")
if not travis:
cached_fingerprint = params.get('CachedFingerprint')
else:
cached_fingerprint = None
if car_params is not None:
car_params = car.CarParams.from_bytes(car_params)
if has_relay:
# Vin query only reliably works thorugh OBDII
bus = 1
cached_params = Params().get("CarParamsCache")
if cached_params is not None:
cached_params = car.CarParams.from_bytes(cached_params)
if cached_params.carName == "mock":
cached_params = None
if cached_params is not None and len(
cached_params.carFw
) > 0 and cached_params.carVin is not VIN_UNKNOWN:
cloudlog.warning("Using cached CarParams")
vin = cached_params.carVin
car_fw = list(cached_params.carFw)
else:
cloudlog.warning("Getting VIN & FW versions")
_, vin = get_vin(logcan, sendcan, bus)
car_fw = get_fw_versions(logcan, sendcan, bus)
fw_candidates = match_fw_to_car(car_fw)
else:
vin = VIN_UNKNOWN
fw_candidates, car_fw = set(), []
cloudlog.warning("VIN %s", vin)
Params().put("CarVin", vin)
finger = gen_empty_fingerprint()
candidate_cars = {i: all_known_cars()
for i in [0, 1]
} # attempt fingerprint on both bus 0 and 1
frame = 0
frame_fingerprint = 10 # 0.1s
car_fingerprint = None
done = False
if cached_fingerprint is not None and use_car_caching: # if we previously identified a car and fingerprint and user hasn't disabled caching
cached_fingerprint = json.loads(cached_fingerprint)
if cached_fingerprint[0] is None or len(cached_fingerprint) < 3:
params.delete('CachedFingerprint')
else:
finger[0] = {
int(key): value
for key, value in cached_fingerprint[2].items()
}
source = car.CarParams.FingerprintSource.can
return (str(cached_fingerprint[0]), finger, vin, car_fw,
cached_fingerprint[1])
while not done:
a = messaging.get_one_can(logcan)
for can in a.can:
# need to independently try to fingerprint both bus 0 and 1 to work
# for the combo black_panda and honda_bosch. Ignore extended messages
# and VIN query response.
# Include bus 2 for toyotas to disambiguate cars using camera messages
# (ideally should be done for all cars but we can't for Honda Bosch)
if can.src in range(0, 4):
finger[can.src][can.address] = len(can.dat)
for b in candidate_cars:
if (can.src == b or (only_toyota_left(candidate_cars[b]) and can.src == 2)) and \
can.address < 0x800 and can.address not in [0x7df, 0x7e0, 0x7e8]:
candidate_cars[b] = eliminate_incompatible_cars(
can, candidate_cars[b])
# if we only have one car choice and the time since we got our first
# message has elapsed, exit
for b in candidate_cars:
# Toyota needs higher time to fingerprint, since DSU does not broadcast immediately
if only_toyota_left(candidate_cars[b]):
frame_fingerprint = 100 # 1s
if len(candidate_cars[b]) == 1:
if frame > frame_fingerprint:
# fingerprint done
car_fingerprint = candidate_cars[b][0]
elif len(
candidate_cars[b]
) < 4: # For the RAV4 2019 and Corolla 2020 LE Fingerprint problem
if frame > 180:
if any(("TOYOTA COROLLA TSS2 2019" in c)
for c in candidate_cars[b]):
car_fingerprint = "TOYOTA COROLLA TSS2 2019"
# bail if no cars left or we've been waiting for more than 2s
failed = all(len(cc) == 0
for cc in candidate_cars.values()) or frame > 200
succeeded = car_fingerprint is not None
done = failed or succeeded
frame += 1
source = car.CarParams.FingerprintSource.can
# If FW query returns exactly 1 candidate, use it
if len(fw_candidates) == 1:
car_fingerprint = list(fw_candidates)[0]
source = car.CarParams.FingerprintSource.fw
fixed_fingerprint = os.environ.get('FINGERPRINT', "")
if len(fixed_fingerprint):
car_fingerprint = fixed_fingerprint
source = car.CarParams.FingerprintSource.fixed
cloudlog.warning("fingerprinted %s", car_fingerprint)
params.put(
"CachedFingerprint",
json.dumps([
car_fingerprint, source,
{int(key): value
for key, value in finger[0].items()}
]))
return car_fingerprint, finger, vin, car_fw, source
def update_panda():
panda = None
panda_dfu = None
cloudlog.info("Connecting to panda")
while True:
# break on normal mode Panda
panda_list = Panda.list()
if len(panda_list) > 0:
cloudlog.info("Panda found, connecting")
panda = Panda(panda_list[0])
break
# flash on DFU mode Panda
panda_dfu = PandaDFU.list()
if len(panda_dfu) > 0:
cloudlog.info("Panda in DFU mode found, flashing recovery")
panda_dfu = PandaDFU(panda_dfu[0])
panda_dfu.recover()
time.sleep(1)
fw_signature = get_expected_signature()
try:
serial = panda.get_serial()[0].decode("utf-8")
except Exception:
serial = None
panda_version = "bootstub" if panda.bootstub else panda.get_version()
panda_signature = b"" if panda.bootstub else panda.get_signature()
cloudlog.warning(
"Panda %s connected, version: %s, signature %s, expected %s" % (
serial,
panda_version,
panda_signature.hex(),
fw_signature.hex(),
))
if panda.bootstub or panda_signature != fw_signature:
cloudlog.info("Panda firmware out of date, update required")
panda.flash()
cloudlog.info("Done flashing")
if panda.bootstub:
bootstub_version = panda.get_version()
cloudlog.info(
f"Flashed firmware not booting, flashing development bootloader. Bootstub version: {bootstub_version}"
)
panda.recover()
cloudlog.info("Done flashing bootloader")
if panda.bootstub:
cloudlog.info("Panda still not booting, exiting")
raise AssertionError
panda_signature = panda.get_signature()
if panda_signature != fw_signature:
cloudlog.info("Version mismatch after flashing, exiting")
raise AssertionError
def update(self, sm, pm, CP, VM):
v_ego = sm['carState'].vEgo
angle_steers = sm['carState'].steeringAngle
active = sm['controlsState'].active
angle_offset = sm['liveParameters'].angleOffset
lca_left = sm['carState'].lcaLeft
lca_right = sm['carState'].lcaRight
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
VM.update_params(sm['liveParameters'].stiffnessFactor, sm['liveParameters'].steerRatio)
curvature_factor = VM.curvature_factor(v_ego)
self.LP.parse_model(sm['model'])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (not one_blinker) or (not self.lane_change_enabled):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if self.lane_change_direction == LaneChangeDirection.left:
torque_applied = sm['carState'].steeringTorque > 0 and sm['carState'].steeringPressed
if CP.autoLcaEnabled and 1.5 > self.pre_auto_LCA_timer > 1.0 and not lca_left:
torque_applied = True # Enable auto LCA only once after 2 sec
else:
torque_applied = sm['carState'].steeringTorque < 0 and sm['carState'].steeringPressed
if CP.autoLcaEnabled and 1.5 > self.pre_auto_LCA_timer > 1.0 and not lca_right:
torque_applied = True # Enable auto LCA only once after 2 sec
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
self.lane_change_state = LaneChangeState.preLaneChange
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
elif torque_applied:
if self.prev_torque_applied or self.lane_change_direction == LaneChangeDirection.left and not lca_left or \
self.lane_change_direction == LaneChangeDirection.right and not lca_right:
self.lane_change_state = LaneChangeState.laneChangeStarting
else:
if self.pre_auto_LCA_timer < 10.:
self.pre_auto_LCA_timer = 10.
else:
if self.pre_auto_LCA_timer > 10.3:
self.prev_torque_applied = True
# bsm
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
if lca_left and self.lane_change_direction == LaneChangeDirection.left and not self.prev_torque_applied:
self.lane_change_BSM = LaneChangeBSM.left
self.lane_change_state = LaneChangeState.preLaneChange
elif lca_right and self.lane_change_direction == LaneChangeDirection.right and not self.prev_torque_applied:
self.lane_change_BSM = LaneChangeBSM.right
self.lane_change_state = LaneChangeState.preLaneChange
else:
# starting
self.lane_change_BSM = LaneChangeBSM.off
if self.lane_change_state == LaneChangeState.laneChangeStarting and lane_change_prob > 0.5:
self.lane_change_state = LaneChangeState.laneChangeFinishing
# starting
#elif self.lane_change_state == LaneChangeState.laneChangeStarting and lane_change_prob > 0.5:
#self.lane_change_state = LaneChangeState.laneChangeFinishing
# finishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing and lane_change_prob < 0.2:
if one_blinker:
self.lane_change_state = LaneChangeState.preLaneChange
else:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [LaneChangeState.off, LaneChangeState.preLaneChange]:
self.lane_change_timer = 0.0
if self.lane_change_BSM == LaneChangeBSM.right:
if not lca_right:
self.lane_change_BSM = LaneChangeBSM.off
if self.lane_change_BSM == LaneChangeBSM.left:
if not lca_left:
self.lane_change_BSM = LaneChangeBSM.off
else:
self.lane_change_timer += DT_MDL
if self.lane_change_state == LaneChangeState.off:
self.pre_auto_LCA_timer = 0.0
self.prev_torque_applied = False
elif not (3. < self.pre_auto_LCA_timer < 10.): # stop afer 3 sec resume from 10 when torque applied
self.pre_auto_LCA_timer += DT_MDL
self.prev_one_blinker = one_blinker
desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Turn off lanes during lane change
if desire == log.PathPlan.Desire.laneChangeRight or desire == log.PathPlan.Desire.laneChangeLeft:
self.LP.l_prob = 0.
self.LP.r_prob = 0.
self.libmpc.init_weights(MPC_COST_LAT.PATH / 10.0, MPC_COST_LAT.LANE, MPC_COST_LAT.HEADING, self.steer_rate_cost)
else:
self.libmpc.init_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE, MPC_COST_LAT.HEADING, self.steer_rate_cost)
self.LP.update_d_poly(v_ego)
# account for actuation delay
self.cur_state = calc_states_after_delay(self.cur_state, v_ego, angle_steers - angle_offset, curvature_factor, VM.sR, CP.steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly), list(self.LP.d_poly),
self.LP.l_prob, self.LP.r_prob, curvature_factor, v_ego_mpc, self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
rate_desired = math.degrees(self.mpc_solution[0].rate[0] * VM.sR)
else:
delta_desired = math.radians(angle_steers - angle_offset) / VM.sR
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(math.degrees(delta_desired * VM.sR) + angle_offset)
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE, MPC_COST_LAT.HEADING, CP.steerRateCost)
self.cur_state[0].delta = math.radians(angle_steers - angle_offset) / VM.sR
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message()
plan_send.init('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'liveParameters', 'model'])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
plan_send.pathPlan.angleOffset = float(sm['liveParameters'].angleOffsetAverage)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.sensorValid = bool(sm['liveParameters'].sensorValid)
plan_send.pathPlan.posenetValid = bool(sm['liveParameters'].posenetValid)
plan_send.pathPlan.desire = desire
plan_send.pathPlan.laneChangeState = self.lane_change_state
plan_send.pathPlan.laneChangeDirection = self.lane_change_direction
plan_send.pathPlan.laneChangeBSM = self.lane_change_BSM
pm.send('pathPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message()
dat.init('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
def uninstall():
cloudlog.warning("uninstalling")
with open('/cache/recovery/command', 'w') as f:
f.write('--wipe_data\n')
# IPowerManager.reboot(confirm=false, reason="recovery", wait=true)
os.system("service call power 16 i32 0 s16 recovery i32 1")
def update(self, sm, pm, CP, VM):
v_ego = sm['carState'].vEgo
stand_still = sm['carState'].standStill
angle_steers = sm['carState'].steeringAngle
active = sm['controlsState'].active
anglesteer_current = sm['controlsState'].angleSteers
anglesteer_desire = sm['controlsState'].angleSteersDes
#live_steerratio = sm['liveParameters'].steerRatio
mode_select = sm['carState'].cruiseState.modeSel
lateral_control_method = sm['controlsState'].lateralControlMethod
if lateral_control_method == 0:
output_scale = sm['controlsState'].lateralControlState.pidState.output
elif lateral_control_method == 1:
output_scale = sm['controlsState'].lateralControlState.indiState.output
elif lateral_control_method == 2:
output_scale = sm['controlsState'].lateralControlState.lqrState.output
angle_offset = sm['liveParameters'].angleOffset
live_steer_ratio = sm['liveParameters'].steerRatio
if live_steer_ratio != CP.steerRatio:
self.steerRatio_range = [CP.steerRatio, live_steer_ratio]
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
self.angle_diff = abs(anglesteer_desire) - abs(anglesteer_current)
if abs(output_scale) >= 1 and v_ego > 8:
self.new_steerRatio_prev = interp(self.angle_diff, self.angle_differ_range, self.steerRatio_range)
if self.new_steerRatio_prev > self.new_steerRatio:
self.new_steerRatio = self.new_steerRatio_prev
#self.new_steer_rate_cost = interp(self.angle_diff, self.angle_differ_range, self.steer_rate_cost_range)
#if abs(output_scale) >= 1 and v_ego > 8 and ((abs(anglesteer_desire) - abs(anglesteer_current)) > 20):
# self.mpc_frame += 1
# if self.mpc_frame % 10 == 0:
# self.new_steerRatio += (round(v_ego, 1) * 0.025)
# if live_steer_ratio != CP.steerRatio:
# if self.new_steerRatio >= live_steer_ratio:
# self.new_steerRatio = live_steer_ratio
# else:
# if self.new_steerRatio >= 17.5:
# self.new_steerRatio = 17.5
# self.mpc_frame = 0
else:
self.mpc_frame += 1
if self.mpc_frame % 10 == 0:
self.new_steerRatio -= 0.1
if self.new_steerRatio <= CP.steerRatio:
self.new_steerRatio = CP.steerRatio
#self.new_steer_rate_cost += 0.02
#if self.new_steer_rate_cost >= CP.steerRateCost:
# self.new_steer_rate_cost = CP.steerRateCost
self.mpc_frame = 0
self.new_steer_actuator_delay = interp(v_ego, self.steer_actuator_delay_vel, self.steer_actuator_delay_range)
# Update vehicle model
x = max(sm['liveParameters'].stiffnessFactor, 0.1)
#sr = max(sm['liveParameters'].steerRatio, 0.1)
sr = max(self.new_steerRatio, 0.1)
VM.update_params(x, sr)
curvature_factor = VM.curvature_factor(v_ego)
self.LP.parse_model(sm['model'])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
#if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (not one_blinker) or (not self.lane_change_enabled):
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (not self.lane_change_enabled) or ( abs(output_scale) >= 0.9 and self.lane_change_timer > 1):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
torque_applied = sm['carState'].steeringPressed and \
((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
blindspot_detected = ((sm['carState'].leftBlindspot and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].rightBlindspot and self.lane_change_direction == LaneChangeDirection.right))
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
self.lane_change_state = LaneChangeState.preLaneChange
self.lane_change_ll_prob = 1.0
self.lane_change_wait_timer = 0
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
self.lane_change_wait_timer += DT_MDL
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
elif not blindspot_detected and (torque_applied or (self.lane_change_auto_delay and self.lane_change_wait_timer > self.lane_change_auto_delay)):
self.lane_change_state = LaneChangeState.laneChangeStarting
# starting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .5s
self.lane_change_adjust_new = interp(v_ego, self.lane_change_adjust_vel, self.lane_change_adjust)
self.lane_change_ll_prob = max(self.lane_change_ll_prob - self.lane_change_adjust_new*DT_MDL, 0.0)
# 98% certainty
if lane_change_prob < 0.03 and self.lane_change_ll_prob < 0.02:
self.lane_change_state = LaneChangeState.laneChangeFinishing
# finishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
# fade in laneline over 1s
self.lane_change_ll_prob = min(self.lane_change_ll_prob + DT_MDL, 1.0)
if one_blinker and self.lane_change_ll_prob > 0.98:
self.lane_change_state = LaneChangeState.preLaneChange
elif self.lane_change_ll_prob > 0.98:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [LaneChangeState.off, LaneChangeState.preLaneChange]:
self.lane_change_timer = 0.0
else:
self.lane_change_timer += DT_MDL
self.prev_one_blinker = one_blinker
desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Turn off lanes during lane change
if desire == log.PathPlan.Desire.laneChangeRight or desire == log.PathPlan.Desire.laneChangeLeft:
self.LP.l_prob *= self.lane_change_ll_prob
self.LP.r_prob *= self.lane_change_ll_prob
self.LP.update_d_poly(v_ego, sm)
# account for actuation delay
self.cur_state = calc_states_after_delay(self.cur_state, v_ego, angle_steers - angle_offset, curvature_factor, VM.sR, self.new_steer_actuator_delay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly), list(self.LP.d_poly),
self.LP.l_prob, self.LP.r_prob, curvature_factor, v_ego_mpc, self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
rate_desired = math.degrees(self.mpc_solution[0].rate[0] * VM.sR)
else:
delta_desired = math.radians(angle_steers - angle_offset) / VM.sR
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(math.degrees(delta_desired * VM.sR) + angle_offset)
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE, MPC_COST_LAT.HEADING, self.new_steer_rate_cost)
self.cur_state[0].delta = math.radians(angle_steers - angle_offset) / VM.sR
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 3
plan_send = messaging.new_message('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'liveParameters', 'model'])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
if self.angle_offset_select == 0:
plan_send.pathPlan.angleOffset = float(sm['liveParameters'].angleOffsetAverage)
else:
plan_send.pathPlan.angleOffset = float(sm['liveParameters'].angleOffset)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.desire = desire
plan_send.pathPlan.laneChangeState = self.lane_change_state
plan_send.pathPlan.laneChangeDirection = self.lane_change_direction
plan_send.pathPlan.steerRatio = VM.sR
plan_send.pathPlan.steerActuatorDelay = self.new_steer_actuator_delay
plan_send.pathPlan.steerRateCost = self.new_steer_rate_cost
plan_send.pathPlan.outputScale = output_scale
if stand_still:
self.standstill_elapsed_time += DT_MDL
else:
self.standstill_elapsed_time = 0.0
plan_send.pathPlan.standstillElapsedTime = int(self.standstill_elapsed_time)
pm.send('pathPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
def fingerprint(logcan, sendcan, has_relay):
fixed_fingerprint = os.environ.get('FINGERPRINT', "")
skip_fw_query = os.environ.get('SKIP_FW_QUERY', False)
if has_relay and not fixed_fingerprint and not skip_fw_query:
# Vin query only reliably works thorugh OBDII
bus = 1
cached_params = Params().get("CarParamsCache")
if cached_params is not None:
cached_params = car.CarParams.from_bytes(cached_params)
if cached_params.carName == "mock":
cached_params = None
if cached_params is not None and len(cached_params.carFw) > 0 and cached_params.carVin is not VIN_UNKNOWN:
cloudlog.warning("Using cached CarParams")
vin = cached_params.carVin
car_fw = list(cached_params.carFw)
else:
cloudlog.warning("Getting VIN & FW versions")
_, vin = get_vin(logcan, sendcan, bus)
car_fw = get_fw_versions(logcan, sendcan, bus)
exact_fw_match, fw_candidates = match_fw_to_car(car_fw)
else:
vin = VIN_UNKNOWN
exact_fw_match, fw_candidates, car_fw = True, set(), []
cloudlog.warning("VIN %s", vin)
Params().put("CarVin", vin)
finger = gen_empty_fingerprint()
candidate_cars = {i: all_known_cars() for i in [0]} # attempt fingerprint on bus 0 only
frame = 0
frame_fingerprint = 10 # 0.1s
car_fingerprint = None
done = False
while not done:
a = get_one_can(logcan)
for can in a.can:
# need to independently try to fingerprint both bus 0 and 1 to work
# for the combo black_panda and honda_bosch. Ignore extended messages
# and VIN query response.
# Include bus 2 for toyotas to disambiguate cars using camera messages
# (ideally should be done for all cars but we can't for Honda Bosch)
if can.src in range(0, 4):
finger[can.src][can.address] = len(can.dat)
for b in candidate_cars:
if (can.src == b or (only_toyota_left(candidate_cars[b]) and can.src == 2)) and \
can.address < 0x800 and can.address not in [0x7df, 0x7e0, 0x7e8]:
candidate_cars[b] = eliminate_incompatible_cars(can, candidate_cars[b])
# if we only have one car choice and the time since we got our first
# message has elapsed, exit
for b in candidate_cars:
# Toyota needs higher time to fingerprint, since DSU does not broadcast immediately
if only_toyota_left(candidate_cars[b]):
frame_fingerprint = 100 # 1s
if len(candidate_cars[b]) == 1 and frame > frame_fingerprint:
# fingerprint done
car_fingerprint = candidate_cars[b][0]
# bail if no cars left or we've been waiting for more than 2s
failed = (all(len(cc) == 0 for cc in candidate_cars.values()) and frame > frame_fingerprint) or frame > 200
succeeded = car_fingerprint is not None
done = failed or succeeded
frame += 1
exact_match = True
source = car.CarParams.FingerprintSource.can
# If FW query returns exactly 1 candidate, use it
if len(fw_candidates) == 1:
car_fingerprint = list(fw_candidates)[0]
source = car.CarParams.FingerprintSource.fw
exact_match = exact_fw_match
if fixed_fingerprint:
car_fingerprint = fixed_fingerprint
source = car.CarParams.FingerprintSource.fixed
cloudlog.warning("fingerprinted %s", car_fingerprint)
return car_fingerprint, finger, vin, car_fw, source, exact_match
def update(self, sm):
v_ego = sm['carState'].vEgo
active = sm['controlsState'].active
measured_curvature = sm['controlsState'].curvature
md = sm['modelV2']
self.LP.parse_model(sm['modelV2'])
if len(md.position.x) == TRAJECTORY_SIZE and len(md.orientation.x) == TRAJECTORY_SIZE:
self.path_xyz = np.column_stack([md.position.x, md.position.y, md.position.z])
self.t_idxs = np.array(md.position.t)
self.plan_yaw = list(md.orientation.z)
if len(md.position.xStd) == TRAJECTORY_SIZE:
self.path_xyz_stds = np.column_stack([md.position.xStd, md.position.yStd, md.position.zStd])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (not one_blinker) or (not self.lane_change_enabled):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
torque_applied = sm['carState'].steeringPressed and \
((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right)) or \
self.auto_lane_change_enabled and \
(AUTO_LCA_START_TIME+0.25) > self.auto_lane_change_timer > AUTO_LCA_START_TIME
blindspot_detected = ((sm['carState'].leftBlindspot and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].rightBlindspot and self.lane_change_direction == LaneChangeDirection.right))
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
self.lane_change_state = LaneChangeState.preLaneChange
self.lane_change_ll_prob = 1.0
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
elif torque_applied and (not blindspot_detected or self.prev_torque_applied):
self.lane_change_state = LaneChangeState.laneChangeStarting
elif torque_applied and blindspot_detected and self.auto_lane_change_timer != 10.0:
self.auto_lane_change_timer = 10.0
elif not torque_applied and self.auto_lane_change_timer == 10.0 and not self.prev_torque_applied:
self.prev_torque_applied = True
# LaneChangeState.laneChangeStarting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .5s
self.lane_change_ll_prob = max(self.lane_change_ll_prob - 2*DT_MDL, 0.0)
# 98% certainty
if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01:
self.lane_change_state = LaneChangeState.laneChangeFinishing
# LaneChangeState.laneChangeFinishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
# fade in laneline over 1s
self.lane_change_ll_prob = min(self.lane_change_ll_prob + DT_MDL, 1.0)
if self.lane_change_ll_prob > 0.99:
self.lane_change_direction = LaneChangeDirection.none
if one_blinker:
self.lane_change_state = LaneChangeState.preLaneChange
else:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in (LaneChangeState.off, LaneChangeState.preLaneChange):
self.lane_change_timer = 0.0
else:
self.lane_change_timer += DT_MDL
if self.lane_change_state == LaneChangeState.off:
self.auto_lane_change_timer = 0.0
self.prev_torque_applied = False
elif self.auto_lane_change_timer < (AUTO_LCA_START_TIME+0.25): # stop afer 3 sec resume from 10 when torque applied
self.auto_lane_change_timer += DT_MDL
self.prev_one_blinker = one_blinker
self.desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Send keep pulse once per second during LaneChangeStart.preLaneChange
if self.lane_change_state in (LaneChangeState.off, LaneChangeState.laneChangeStarting):
self.keep_pulse_timer = 0.0
elif self.lane_change_state == LaneChangeState.preLaneChange:
self.keep_pulse_timer += DT_MDL
if self.keep_pulse_timer > 1.0:
self.keep_pulse_timer = 0.0
elif self.desire in (log.LateralPlan.Desire.keepLeft, log.LateralPlan.Desire.keepRight):
self.desire = log.LateralPlan.Desire.none
# Turn off lanes during lane change
if self.desire == log.LateralPlan.Desire.laneChangeRight or self.desire == log.LateralPlan.Desire.laneChangeLeft:
self.LP.lll_prob *= self.lane_change_ll_prob
self.LP.rll_prob *= self.lane_change_ll_prob
if self.use_lanelines:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.lat_mpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, ntune_common_get('steerRateCost'))
else:
d_path_xyz = self.path_xyz
path_cost = np.clip(abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5, 1.5) * MPC_COST_LAT.PATH
# Heading cost is useful at low speed, otherwise end of plan can be off-heading
heading_cost = interp(v_ego, [5.0, 10.0], [MPC_COST_LAT.HEADING, 0.0])
self.lat_mpc.set_weights(path_cost, heading_cost, ntune_common_get('steerRateCost'))
y_pts = np.interp(v_ego * self.t_idxs[:LAT_MPC_N + 1], np.linalg.norm(d_path_xyz, axis=1), d_path_xyz[:,1])
heading_pts = np.interp(v_ego * self.t_idxs[:LAT_MPC_N + 1], np.linalg.norm(self.path_xyz, axis=1), self.plan_yaw)
self.y_pts = y_pts
assert len(y_pts) == LAT_MPC_N + 1
assert len(heading_pts) == LAT_MPC_N + 1
# self.x0[4] = v_ego
p = np.array([v_ego, CAR_ROTATION_RADIUS])
self.lat_mpc.run(self.x0,
p,
y_pts,
heading_pts)
# init state for next
self.x0[3] = interp(DT_MDL, self.t_idxs[:LAT_MPC_N + 1], self.lat_mpc.x_sol[:, 3])
# Check for infeasible MPC solution
mpc_nans = np.isnan(self.lat_mpc.x_sol[:, 3]).any()
t = sec_since_boot()
if mpc_nans or self.lat_mpc.solution_status != 0:
self.reset_mpc()
self.x0[3] = measured_curvature
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.lat_mpc.cost > 20000. or mpc_nans:
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
def update(self, sm, pm, CP, VM):
v_ego = sm['carState'].vEgo
angle_steers = sm['carState'].steeringAngle
active = sm['controlsState'].active
angle_offset = sm['liveParameters'].angleOffset
self.LP.update(v_ego, sm['model'])
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
VM.update_params(sm['liveParameters'].stiffnessFactor,
sm['liveParameters'].steerRatio)
curvature_factor = VM.curvature_factor(v_ego)
# TODO: Check for active, override, and saturation
# if active:
# self.path_offset_i += self.LP.d_poly[3] / (60.0 * 20.0)
# self.path_offset_i = clip(self.path_offset_i, -0.5, 0.5)
# self.LP.d_poly[3] += self.path_offset_i
# else:
# self.path_offset_i = 0.0
# account for actuation delay
self.cur_state = calc_states_after_delay(self.cur_state, v_ego,
angle_steers - angle_offset,
curvature_factor, VM.sR,
CP.steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly),
list(self.LP.d_poly), self.LP.l_prob,
self.LP.r_prob, curvature_factor, v_ego_mpc,
self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
rate_desired = math.degrees(self.mpc_solution[0].rate[0] * VM.sR)
else:
delta_desired = math.radians(angle_steers - angle_offset) / VM.sR
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * VM.sR) + angle_offset)
# Check for infeasable MPC solution
mpc_nans = np.any(np.isnan(list(self.mpc_solution[0].delta)))
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, CP.steerRateCost)
self.cur_state[0].delta = math.radians(angle_steers -
angle_offset) / VM.sR
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message()
plan_send.init('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=[
'carState', 'controlsState', 'liveParameters', 'model'
])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
plan_send.pathPlan.angleOffset = float(
sm['liveParameters'].angleOffsetAverage)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.sensorValid = bool(sm['liveParameters'].sensorValid)
plan_send.pathPlan.posenetValid = bool(
sm['liveParameters'].posenetValid)
pm.send('pathPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message()
dat.init('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
def update(self, pm, CS, lead):
v_ego = CS.vEgo
# Setup current mpc state
self.cur_state[0].x_ego = 0.0
if lead is not None and lead.status:
self.x_lead = x_lead = lead.dRel
self.v_lead = v_lead = max(0.0, lead.vLead)
a_lead = lead.aLeadK
if (v_lead < 0.1 or -a_lead / 2.0 > v_lead):
v_lead = 0.0
a_lead = 0.0
self.a_lead_tau = lead.aLeadTau
self.new_lead = False
if not self.prev_lead_status or abs(x_lead -
self.prev_lead_x) > 2.5:
self.libmpc.init_with_simulation(self.v_mpc, x_lead, v_lead,
a_lead, self.a_lead_tau)
self.new_lead = True
self.prev_lead_status = True
self.prev_lead_x = x_lead
self.cur_state[0].x_l = x_lead
self.cur_state[0].v_l = v_lead
else:
self.prev_lead_status = False
# Fake a fast lead car, so mpc keeps running
self.cur_state[0].x_l = 50.0
self.cur_state[0].v_l = v_ego + 10.0
a_lead = 0.0
self.a_lead_tau = _LEAD_ACCEL_TAU
self.v_lead = 0.
self.x_lead = 0.
# Calculate mpc
t = sec_since_boot()
maxTR = interp(v_ego, BpTr, TrY)
if v_ego < 25. or self.x_lead < 25.:
maxTR = max(maxTR, interp((self.v_lead - v_ego), BpvlTr, TrvlY))
if self.v_lead < v_ego + .6 and v_ego > .3:
if self.x_lead < 25.:
maxTR *= 1.1
TR = self.last_TR + .01
elif self.v_lead - v_ego < -10.:
maxTR *= 0.75
TR = self.last_TR + .005
else:
TR = self.last_TR + .005
else:
if self.x_lead > 65.:
maxTR *= 0.85
TR = self.last_TR - .025
else:
TR = self.last_TR - .0025
TR = clip(TR, 0.7, maxTR)
if v_ego < 5. and self.v_lead > v_ego + .5:
TR = 0.25
TR = clip(TR, 0.25, maxTR)
self.last_TR = TR
n_its = self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
self.a_lead_tau, a_lead, TR)
duration = int((sec_since_boot() - t) * 1e9)
if LOG_MPC:
self.send_mpc_solution(pm, n_its, duration)
# Get solution. MPC timestep is 0.2 s, so interpolation to 0.05 s is needed
self.v_mpc = self.mpc_solution[0].v_ego[1]
self.a_mpc = self.mpc_solution[0].a_ego[1]
self.v_mpc_future = self.mpc_solution[0].v_ego[10]
# Reset if NaN or goes through lead car
crashing = any(lead - ego < -50 for (
lead,
ego) in zip(self.mpc_solution[0].x_l, self.mpc_solution[0].x_ego))
nans = any(math.isnan(x) for x in self.mpc_solution[0].v_ego)
backwards = min(self.mpc_solution[0].v_ego) < -0.01
if ((backwards or crashing) and self.prev_lead_status) or nans:
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning(
"Longitudinal mpc %d reset - backwards: %s crashing: %s nan: %s"
% (self.mpc_id, backwards, crashing, nans))
self.libmpc.init(MPC_COST_LONG.TTC, MPC_COST_LONG.DISTANCE,
MPC_COST_LONG.ACCELERATION, MPC_COST_LONG.JERK)
self.cur_state[0].v_ego = v_ego
self.cur_state[0].a_ego = 0.0
self.v_mpc = v_ego
self.a_mpc = CS.aEgo
self.prev_lead_status = False
def update(self, sm, pm, CP, VM):
limit_steers1 = 0
limit_steers2 = 0
debug_status = 0
v_ego = sm['carState'].vEgo
angle_steers = sm['carState'].steeringAngle
active = sm['controlsState'].active
steeringPressed = sm['carState'].steeringPressed
saturated_steering = sm['controlsState'].steerSaturated
live_steerratio = sm['liveParameters'].steerRatio
mode_select = sm['carState'].cruiseState.modeSel
steeringTorque = sm['carState'].steeringTorque
angle_offset = sm['liveParameters'].angleOffset
model_sum = sm['controlsState'].modelSum
v_ego_kph = v_ego * CV.MS_TO_KPH
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
if saturated_steering:
self.mpc_frame += 1
if self.mpc_frame % 10 == 0:
self.new_steerRatio += 0.1
if self.new_steerRatio >= 16.5:
self.new_steerRatio = 16.5
self.mpc_frame = 0
else:
self.mpc_frame += 1
if self.mpc_frame % 20 == 0:
self.new_steerRatio -= 0.1
if self.new_steerRatio <= CP.steerRatio:
self.new_steerRatio = CP.steerRatio
self.mpc_frame = 0
# Update vehicle model
x = max(sm['liveParameters'].stiffnessFactor, 0.1)
#sr = max(sm['liveParameters'].steerRatio, 0.1)
sr = max(self.new_steerRatio, 0.1)
VM.update_params(x, sr)
curvature_factor = VM.curvature_factor(v_ego)
self.LP.parse_model(sm['model'])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (not one_blinker) or (not self.lane_change_enabled):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
torque_applied = sm['carState'].steeringPressed and \
((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
blindspot_detected = ((sm['carState'].leftBlindspot and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].rightBlindspot and self.lane_change_direction == LaneChangeDirection.right))
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
self.lane_change_state = LaneChangeState.preLaneChange
self.lane_change_ll_prob = 1.0
self.lane_change_wait_timer = 0
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
self.lane_change_wait_timer += DT_MDL
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
elif not blindspot_detected and (torque_applied or (self.lane_change_auto_delay and self.lane_change_wait_timer > self.lane_change_auto_delay)):
self.lane_change_state = LaneChangeState.laneChangeStarting
# starting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .5s
self.lane_change_adjust_new = interp(v_ego, self.lane_change_adjust_vel, self.lane_change_adjust)
self.lane_change_ll_prob = max(self.lane_change_ll_prob - self.lane_change_adjust_new*DT_MDL, 0.0)
# 98% certainty
if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01:
self.lane_change_state = LaneChangeState.laneChangeFinishing
# finishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
# fade in laneline over 1s
self.lane_change_ll_prob = min(self.lane_change_ll_prob + DT_MDL, 1.0)
if one_blinker and self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.laneChangeDone
# done
elif self.lane_change_state == LaneChangeState.laneChangeDone:
if not one_blinker:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [LaneChangeState.off, LaneChangeState.preLaneChange]:
self.lane_change_timer = 0.0
else:
self.lane_change_timer += DT_MDL
self.prev_one_blinker = one_blinker
desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Turn off lanes during lane change
if desire == log.PathPlan.Desire.laneChangeRight or desire == log.PathPlan.Desire.laneChangeLeft:
self.LP.l_prob *= self.lane_change_ll_prob
self.LP.r_prob *= self.lane_change_ll_prob
self.LP.update_d_poly(v_ego, sm)
# account for actuation delay
if mode_select == 3:
self.cur_state = calc_states_after_delay(self.cur_state, v_ego, angle_steers - angle_offset, curvature_factor, VM.sR, (CP.steerActuatorDelay + 0.05))
else:
self.cur_state = calc_states_after_delay(self.cur_state, v_ego, angle_steers - angle_offset, curvature_factor, VM.sR, CP.steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly), list(self.LP.d_poly),
self.LP.l_prob, self.LP.r_prob, curvature_factor, v_ego_mpc, self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
rate_desired = math.degrees(self.mpc_solution[0].rate[0] * VM.sR)
else:
delta_desired = math.radians(angle_steers - angle_offset) / VM.sR
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(math.degrees(delta_desired * VM.sR) + angle_offset)
# Atom
# org_angle_steers_des = self.angle_steers_des_mpc
# delta_steer = org_angle_steers_des - angle_steers
# if self.lane_change_state == LaneChangeState.laneChangeStarting:
# debug_status = 0
# xp = [40,70]
# fp2 = [5,10]
# limit_steers = interp( v_ego_kph, xp, fp2 )
# self.angle_steers_des_mpc = self.limit_ctrl( org_angle_steers_des, limit_steers, angle_steers )
# elif steeringPressed:
# debug_status = 1
# if angle_steers > 10 and steeringTorque > 0:
# delta_steer = max( delta_steer, 0 )
# delta_steer = min( delta_steer, DST_ANGLE_LIMIT )
# self.angle_steers_des_mpc = angle_steers + delta_steer
# elif angle_steers < -10 and steeringTorque < 0:
# delta_steer = min( delta_steer, 0 )
# delta_steer = max( delta_steer, -DST_ANGLE_LIMIT )
# self.angle_steers_des_mpc = angle_steers + delta_steer
# else:
# debug_status = 2
# if steeringTorque < 0: # right
# if delta_steer > 0:
# self.angle_steers_des_mpc = self.limit_ctrl( org_angle_steers_des, DST_ANGLE_LIMIT, angle_steers )
# elif steeringTorque > 0: # left
# if delta_steer < 0:
# self.angle_steers_des_mpc = self.limit_ctrl( org_angle_steers_des, DST_ANGLE_LIMIT, angle_steers )
# elif v_ego_kph < 20 : #and abs(angle_steers) < 5.0 :
# debug_status = 3
# # 저속 와리가리 제어.
# xp = [5,10,20]
# fp2 = [1,3,7]
# limit_steers = interp( v_ego_kph, xp, fp2 )
# self.angle_steers_des_mpc = self.limit_ctrl( org_angle_steers_des, limit_steers, angle_steers )
# elif v_ego_kph > 85:
# debug_status = 4
# pass
# elif abs(angle_steers) > 25:
# # #최대 허용 조향각 제어 로직 1.
# debug_status = 5
# xp = [-30,-20,-10,-5,0,5,10,20,30] # 5=>약12도, 10=>28 15=>35, 30=>52
# fp1 = [ 3, 5, 7, 9,11,13,15,13,11,9] # +
# fp2 = [ 9,11,13,15,13,11, 9, 7, 5,3] # -
# # fp1 = [ 5, 7, 9,11,13,15,17,15,12] # +
# # fp2 = [12,15,17,15,13,11, 9, 7, 5] # -
# # xp = [-40,-30,-20,-10,-5,0,5,10,20,30,40] # 5=>약12도, 10=>28 15=>35, 30=>52
# # fp1 = [ 3, 5, 7, 9,11,13,15,17,15,12,10] # +
# # fp2 = [10,12,15,17,15,13,11, 9, 7, 5, 3] # -
# limit_steers1 = interp( model_sum, xp, fp1 ) # +
# limit_steers2 = interp( model_sum, xp, fp2 ) # -
# self.angle_steers_des_mpc = self.limit_ctrl1( org_angle_steers_des, limit_steers1, limit_steers2, angle_steers )
# str1 = '#/{} CVs/{} LS1/{} LS2/{} Ang/{} oDES/{} delta1/{} fDES/{} '.format(
# debug_status, model_sum, limit_steers1, limit_steers2, angle_steers, org_angle_steers_des, delta_steer, self.angle_steers_des_mpc)
# # Conan : 최대 허용 조향각 제어 로직 2.
# delta_steer2 = self.angle_steers_des_mpc - angle_steers
# if delta_steer2 > DST_ANGLE_LIMIT:
# p_angle_steers = angle_steers + DST_ANGLE_LIMIT
# self.angle_steers_des_mpc = p_angle_steers
# elif delta_steer2 < -DST_ANGLE_LIMIT:
# m_angle_steers = angle_steers - DST_ANGLE_LIMIT
# self.angle_steers_des_mpc = m_angle_steers
# str2 = 'delta2/{} fDES2/{}'.format(
# delta_steer2, self.angle_steers_des_mpc)
# self.trRapidCurv.add( str1 + str2 )
# Hoya : 가변 sR rate_cost
# self.sr_boost_bp = [ 10.0, 15.0, 20.0, 30.0]
# self.sR_Cost = [ 1.00, 0.75, 0.60, 0.30]
# steerRateCost = interp(abs(angle_steers), self.sr_boost_bp, self.sR_Cost)
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE, MPC_COST_LAT.HEADING, CP.steerRateCost) # CP.steerRateCost < steerRateCost
self.cur_state[0].delta = math.radians(angle_steers - angle_offset) / VM.sR
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 3
plan_send = messaging.new_message('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'liveParameters', 'model'])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
plan_send.pathPlan.angleOffset = float(sm['liveParameters'].angleOffset)
#plan_send.pathPlan.angleOffset = float(sm['liveParameters'].angleOffsetAverage)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.desire = desire
plan_send.pathPlan.laneChangeState = self.lane_change_state
plan_send.pathPlan.laneChangeDirection = self.lane_change_direction
plan_send.pathPlan.steerRatio = VM.sR
plan_send.pathPlan.steerActuatorDelay = CP.steerActuatorDelay
pm.send('pathPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
def update_panda():
repo_version = get_expected_version()
panda = None
panda_dfu = None
cloudlog.info("Connecting to panda")
while True:
# break on normal mode Panda
panda_list = Panda.list()
if len(panda_list) > 0:
cloudlog.info("Panda found, connecting")
panda = Panda(panda_list[0])
break
# flash on DFU mode Panda
panda_dfu = PandaDFU.list()
if len(panda_dfu) > 0:
cloudlog.info("Panda in DFU mode found, flashing recovery")
panda_dfu = PandaDFU(panda_dfu[0])
panda_dfu.recover()
print("waiting for board...")
time.sleep(1)
try:
serial = panda.get_serial()[0].decode("utf-8")
except Exception:
serial = None
current_version = "bootstub" if panda.bootstub else panda.get_version()
cloudlog.warning("Panda %s connected, version: %s, expected %s" %
(serial, current_version, repo_version))
if panda.bootstub or not current_version.startswith(repo_version):
cloudlog.info("Panda firmware out of date, update required")
signed_fn = os.path.join(BASEDIR, "board", "obj", "panda.bin.signed")
if os.path.exists(signed_fn):
cloudlog.info("Flashing signed firmware")
panda.flash(fn=signed_fn)
else:
cloudlog.info("Building and flashing unsigned firmware")
panda.flash()
cloudlog.info("Done flashing")
if panda.bootstub:
cloudlog.info(
"Flashed firmware not booting, flashing development bootloader")
panda.recover()
cloudlog.info("Done flashing bootloader")
if panda.bootstub:
cloudlog.info("Panda still not booting, exiting")
raise AssertionError
version = panda.get_version()
if not version.startswith(repo_version):
cloudlog.info("Version mismatch after flashing, exiting")
raise AssertionError
def fingerprint(logcan, timeout):
if os.getenv("SIMULATOR2") is not None:
return ("simulator2", None)
elif os.getenv("SIMULATOR") is not None:
return ("simulator", None)
params = Params()
cached_fingerprint = params.get('CachedFingerprint')
if cached_fingerprint is not None and kegman.get(
"useCarCaching", True
): # if we previously identified a car and fingerprint and user hasn't disabled caching
cached_fingerprint = json.loads(cached_fingerprint)
try:
with open("/data/kegman.json", "r") as f:
cloudlog.warning(str(f.read()))
except:
pass
try:
with open("/data/params/d/ControlsParams", "r") as f:
cloudlog.warning(f.read())
except:
pass
try:
with open("/data/params/d/LiveParameters", "r") as f:
cloudlog.warning(f.read())
except:
pass
return (str(cached_fingerprint[0]), {
long(key): value
for key, value in cached_fingerprint[1].items()
}) # not sure if dict of longs is required
cloudlog.warning("waiting for fingerprint...")
candidate_cars = all_known_cars()
finger = {}
st = None
st_passive = sec_since_boot() # only relevant when passive
can_seen = False
while 1:
for a in messaging.drain_sock(logcan):
for can in a.can:
can_seen = True
# ignore everything not on bus 0 and with more than 11 bits,
# which are ussually sporadic and hard to include in fingerprints
if can.src == 0 and can.address < 0x800:
finger[can.address] = len(can.dat)
candidate_cars = eliminate_incompatible_cars(
can, candidate_cars)
if st is None and can_seen:
st = sec_since_boot() # start time
ts = sec_since_boot()
# if we only have one car choice and the time_fingerprint since we got our first
# message has elapsed, exit. Toyota needs higher time_fingerprint, since DSU does not
# broadcast immediately
if len(candidate_cars) == 1 and st is not None:
# TODO: better way to decide to wait more if Toyota
time_fingerprint = 1.0 if ("TOYOTA" in candidate_cars[0] or "LEXUS"
in candidate_cars[0]) else 0.1
if (ts - st) > time_fingerprint:
break
# bail if no cars left or we've been waiting too long
elif len(candidate_cars) == 0 or (timeout and
(ts - st_passive) > timeout):
return None, finger
time.sleep(0.01)
try:
with open("/data/kegman.json", "r") as f:
cloudlog.warning(str(f.read()))
except:
pass
try:
with open("/data/params/d/ControlsParams", "r") as f:
cloudlog.warning(f.read())
except:
pass
try:
with open("/data/params/d/LiveParameters", "r") as f:
cloudlog.warning(f.read())
except:
pass
cloudlog.warning("fingerprinted %s", candidate_cars[0])
params.put("CachedFingerprint",
json.dumps([
candidate_cars[0],
{int(key): value
for key, value in finger.items()}
])) # probably can remove long to int conversion
return (candidate_cars[0], finger)
def get_fw_versions(logcan,
sendcan,
bus,
extra=None,
timeout=0.1,
debug=False,
progress=False):
ecu_types = {}
# Extract ECU adresses to query from fingerprints
# ECUs using a subadress need be queried one by one, the rest can be done in parallel
addrs = []
parallel_addrs = []
versions = get_attr_from_cars('FW_VERSIONS', combine_brands=False)
if extra is not None:
versions.update(extra)
for brand, brand_versions in versions.items():
for c in brand_versions.values():
for ecu_type, addr, sub_addr in c.keys():
a = (brand, addr, sub_addr)
if a not in ecu_types:
ecu_types[(addr, sub_addr)] = ecu_type
if sub_addr is None:
if a not in parallel_addrs:
parallel_addrs.append(a)
else:
if [a] not in addrs:
addrs.append([a])
addrs.insert(0, parallel_addrs)
fw_versions = {}
for i, addr in enumerate(tqdm(addrs, disable=not progress)):
for addr_chunk in chunks(addr):
for brand, request, response in REQUESTS:
try:
addrs = [(a, s) for (b, a, s) in addr_chunk
if b in (brand, 'any')]
if addrs:
query = IsoTpParallelQuery(sendcan,
logcan,
bus,
addrs,
request,
response,
debug=debug)
t = 2 * timeout if i == 0 else timeout
fw_versions.update(query.get_data(t))
except Exception:
cloudlog.warning(
f"FW query exception: {traceback.format_exc()}")
# Build capnp list to put into CarParams
car_fw = []
for addr, version in fw_versions.items():
f = car.CarParams.CarFw.new_message()
f.ecu = ecu_types[addr]
f.fwVersion = version
f.address = addr[0]
if addr[1] is not None:
f.subAddress = addr[1]
car_fw.append(f)
return car_fw
def update(self, CP, VM, CS, md, live100):
v_ego = CS.carState.vEgo
angle_steers = CS.carState.steeringAngle
active = live100.live100.active
angle_offset = live100.live100.angleOffset
self.MP.update(v_ego, md)
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
curvature_factor = VM.curvature_factor(v_ego)
l_poly = libmpc_py.ffi.new("double[4]", list(self.MP.l_poly))
r_poly = libmpc_py.ffi.new("double[4]", list(self.MP.r_poly))
p_poly = libmpc_py.ffi.new("double[4]", list(self.MP.p_poly))
# account for actuation delay
#self.cur_state = calc_states_after_delay(self.cur_state, v_ego, angle_steers, curvature_factor, CP.steerRatio, CP.steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution, l_poly, r_poly,
p_poly, self.MP.l_prob, self.MP.r_prob,
self.MP.p_prob, curvature_factor, v_ego_mpc,
self.MP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
else:
delta_desired = math.radians(angle_steers -
angle_offset) / CP.steerRatio
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * CP.steerRatio) + angle_offset)
# Check for infeasable MPC solution
mpc_nans = np.any(np.isnan(list(self.mpc_solution[0].delta)))
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, CP.steerRateCost)
self.cur_state[0].delta = math.radians(
angle_steers) / CP.steerRatio
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.invalid_counter += 1
else:
self.invalid_counter = 0
plan_valid = self.invalid_counter < 2
plan_send = messaging.new_message()
plan_send.init('pathPlan')
plan_send.pathPlan.laneWidth = float(self.MP.lane_width)
plan_send.pathPlan.dPoly = map(float, self.MP.d_poly)
plan_send.pathPlan.cPoly = map(float, self.MP.c_poly)
plan_send.pathPlan.cProb = float(self.MP.c_prob)
plan_send.pathPlan.lPoly = map(float, l_poly)
plan_send.pathPlan.lProb = float(self.MP.l_prob)
plan_send.pathPlan.rPoly = map(float, r_poly)
plan_send.pathPlan.rProb = float(self.MP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.valid = bool(plan_valid)
self.plan.send(plan_send.to_bytes())
dat = messaging.new_message()
dat.init('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
self.livempc.send(dat.to_bytes())
def thermald_thread():
pm = messaging.PubMaster(['deviceState'])
pandaState_timeout = int(1000 * 2.5 * DT_TRML) # 2.5x the expected pandaState frequency
pandaState_sock = messaging.sub_sock('pandaStates', timeout=pandaState_timeout)
sm = messaging.SubMaster(["peripheralState", "gpsLocationExternal", "managerState"])
fan_speed = 0
count = 0
startup_conditions = {
"ignition": False,
}
startup_conditions_prev = startup_conditions.copy()
off_ts = None
started_ts = None
started_seen = False
thermal_status = ThermalStatus.green
usb_power = True
network_type = NetworkType.none
network_strength = NetworkStrength.unknown
network_info = None
modem_version = None
registered_count = 0
nvme_temps = None
modem_temps = None
current_filter = FirstOrderFilter(0., CURRENT_TAU, DT_TRML)
temp_filter = FirstOrderFilter(0., TEMP_TAU, DT_TRML)
pandaState_prev = None
should_start_prev = False
in_car = False
handle_fan = None
is_uno = False
ui_running_prev = False
params = Params()
power_monitor = PowerMonitoring()
no_panda_cnt = 0
HARDWARE.initialize_hardware()
thermal_config = HARDWARE.get_thermal_config()
# TODO: use PI controller for UNO
controller = PIController(k_p=0, k_i=2e-3, neg_limit=-80, pos_limit=0, rate=(1 / DT_TRML))
# Leave flag for loggerd to indicate device was left onroad
if params.get_bool("IsOnroad"):
params.put_bool("BootedOnroad", True)
while True:
pandaStates = messaging.recv_sock(pandaState_sock, wait=True)
sm.update(0)
peripheralState = sm['peripheralState']
msg = read_thermal(thermal_config)
if pandaStates is not None and len(pandaStates.pandaStates) > 0:
pandaState = pandaStates.pandaStates[0]
# If we lose connection to the panda, wait 5 seconds before going offroad
if pandaState.pandaType == log.PandaState.PandaType.unknown:
no_panda_cnt += 1
if no_panda_cnt > DISCONNECT_TIMEOUT / DT_TRML:
if startup_conditions["ignition"]:
cloudlog.error("Lost panda connection while onroad")
startup_conditions["ignition"] = False
else:
no_panda_cnt = 0
startup_conditions["ignition"] = pandaState.ignitionLine or pandaState.ignitionCan
in_car = pandaState.harnessStatus != log.PandaState.HarnessStatus.notConnected
usb_power = peripheralState.usbPowerMode != log.PeripheralState.UsbPowerMode.client
# Setup fan handler on first connect to panda
if handle_fan is None and peripheralState.pandaType != log.PandaState.PandaType.unknown:
is_uno = peripheralState.pandaType == log.PandaState.PandaType.uno
if TICI:
cloudlog.info("Setting up TICI fan handler")
handle_fan = handle_fan_tici
elif is_uno or PC:
cloudlog.info("Setting up UNO fan handler")
handle_fan = handle_fan_uno
else:
cloudlog.info("Setting up EON fan handler")
setup_eon_fan()
handle_fan = handle_fan_eon
# Handle disconnect
if pandaState_prev is not None:
if pandaState.pandaType == log.PandaState.PandaType.unknown and \
pandaState_prev.pandaType != log.PandaState.PandaType.unknown:
params.clear_all(ParamKeyType.CLEAR_ON_PANDA_DISCONNECT)
pandaState_prev = pandaState
# these are expensive calls. update every 10s
if (count % int(10. / DT_TRML)) == 0:
try:
network_type = HARDWARE.get_network_type()
network_strength = HARDWARE.get_network_strength(network_type)
network_info = HARDWARE.get_network_info() # pylint: disable=assignment-from-none
nvme_temps = HARDWARE.get_nvme_temperatures()
modem_temps = HARDWARE.get_modem_temperatures()
# Log modem version once
if modem_version is None:
modem_version = HARDWARE.get_modem_version() # pylint: disable=assignment-from-none
if modem_version is not None:
cloudlog.warning(f"Modem version: {modem_version}")
if TICI and (network_info.get('state', None) == "REGISTERED"):
registered_count += 1
else:
registered_count = 0
if registered_count > 10:
cloudlog.warning(f"Modem stuck in registered state {network_info}. nmcli conn up lte")
os.system("nmcli conn up lte")
registered_count = 0
except Exception:
cloudlog.exception("Error getting network status")
msg.deviceState.freeSpacePercent = get_available_percent(default=100.0)
msg.deviceState.memoryUsagePercent = int(round(psutil.virtual_memory().percent))
msg.deviceState.cpuUsagePercent = [int(round(n)) for n in psutil.cpu_percent(percpu=True)]
msg.deviceState.gpuUsagePercent = int(round(HARDWARE.get_gpu_usage_percent()))
msg.deviceState.networkType = network_type
msg.deviceState.networkStrength = network_strength
if network_info is not None:
msg.deviceState.networkInfo = network_info
if nvme_temps is not None:
msg.deviceState.nvmeTempC = nvme_temps
if modem_temps is not None:
msg.deviceState.modemTempC = modem_temps
msg.deviceState.batteryPercent = HARDWARE.get_battery_capacity()
msg.deviceState.batteryCurrent = HARDWARE.get_battery_current()
msg.deviceState.usbOnline = HARDWARE.get_usb_present()
current_filter.update(msg.deviceState.batteryCurrent / 1e6)
max_comp_temp = temp_filter.update(
max(max(msg.deviceState.cpuTempC), msg.deviceState.memoryTempC, max(msg.deviceState.gpuTempC))
)
if handle_fan is not None:
fan_speed = handle_fan(controller, max_comp_temp, fan_speed, startup_conditions["ignition"])
msg.deviceState.fanSpeedPercentDesired = fan_speed
is_offroad_for_5_min = (started_ts is None) and ((not started_seen) or (off_ts is None) or (sec_since_boot() - off_ts > 60 * 5))
if is_offroad_for_5_min and max_comp_temp > OFFROAD_DANGER_TEMP:
# If device is offroad we want to cool down before going onroad
# since going onroad increases load and can make temps go over 107
thermal_status = ThermalStatus.danger
else:
current_band = THERMAL_BANDS[thermal_status]
band_idx = list(THERMAL_BANDS.keys()).index(thermal_status)
if current_band.min_temp is not None and max_comp_temp < current_band.min_temp:
thermal_status = list(THERMAL_BANDS.keys())[band_idx - 1]
elif current_band.max_temp is not None and max_comp_temp > current_band.max_temp:
thermal_status = list(THERMAL_BANDS.keys())[band_idx + 1]
# **** starting logic ****
# Check for last update time and display alerts if needed
now = datetime.datetime.utcnow()
# show invalid date/time alert
startup_conditions["time_valid"] = (now.year > 2020) or (now.year == 2020 and now.month >= 10)
set_offroad_alert_if_changed("Offroad_InvalidTime", (not startup_conditions["time_valid"]))
# Show update prompt
try:
last_update = datetime.datetime.fromisoformat(params.get("LastUpdateTime", encoding='utf8'))
except (TypeError, ValueError):
last_update = now
dt = now - last_update
update_failed_count = params.get("UpdateFailedCount")
update_failed_count = 0 if update_failed_count is None else int(update_failed_count)
last_update_exception = params.get("LastUpdateException", encoding='utf8')
if update_failed_count > 15 and last_update_exception is not None:
if tested_branch:
extra_text = "Ensure the software is correctly installed"
else:
extra_text = last_update_exception
set_offroad_alert_if_changed("Offroad_ConnectivityNeeded", False)
set_offroad_alert_if_changed("Offroad_ConnectivityNeededPrompt", False)
set_offroad_alert_if_changed("Offroad_UpdateFailed", True, extra_text=extra_text)
elif dt.days > DAYS_NO_CONNECTIVITY_MAX and update_failed_count > 1:
set_offroad_alert_if_changed("Offroad_UpdateFailed", False)
set_offroad_alert_if_changed("Offroad_ConnectivityNeededPrompt", False)
set_offroad_alert_if_changed("Offroad_ConnectivityNeeded", True)
elif dt.days > DAYS_NO_CONNECTIVITY_PROMPT:
remaining_time = str(max(DAYS_NO_CONNECTIVITY_MAX - dt.days, 0))
set_offroad_alert_if_changed("Offroad_UpdateFailed", False)
set_offroad_alert_if_changed("Offroad_ConnectivityNeeded", False)
set_offroad_alert_if_changed("Offroad_ConnectivityNeededPrompt", True, extra_text=f"{remaining_time} days.")
else:
set_offroad_alert_if_changed("Offroad_UpdateFailed", False)
set_offroad_alert_if_changed("Offroad_ConnectivityNeeded", False)
set_offroad_alert_if_changed("Offroad_ConnectivityNeededPrompt", False)
startup_conditions["up_to_date"] = params.get("Offroad_ConnectivityNeeded") is None or params.get_bool("DisableUpdates")
startup_conditions["not_uninstalling"] = not params.get_bool("DoUninstall")
startup_conditions["accepted_terms"] = params.get("HasAcceptedTerms") == terms_version
panda_signature = params.get("PandaFirmware")
startup_conditions["fw_version_match"] = (panda_signature is None) or (panda_signature == FW_SIGNATURE) # don't show alert is no panda is connected (None)
set_offroad_alert_if_changed("Offroad_PandaFirmwareMismatch", (not startup_conditions["fw_version_match"]))
# with 2% left, we killall, otherwise the phone will take a long time to boot
startup_conditions["free_space"] = msg.deviceState.freeSpacePercent > 2
startup_conditions["completed_training"] = params.get("CompletedTrainingVersion") == training_version or \
params.get_bool("Passive")
startup_conditions["not_driver_view"] = not params.get_bool("IsDriverViewEnabled")
startup_conditions["not_taking_snapshot"] = not params.get_bool("IsTakingSnapshot")
# if any CPU gets above 107 or the battery gets above 63, kill all processes
# controls will warn with CPU above 95 or battery above 60
startup_conditions["device_temp_good"] = thermal_status < ThermalStatus.danger
set_offroad_alert_if_changed("Offroad_TemperatureTooHigh", (not startup_conditions["device_temp_good"]))
if TICI:
set_offroad_alert_if_changed("Offroad_NvmeMissing", (not Path("/data/media").is_mount()))
# Handle offroad/onroad transition
should_start = all(startup_conditions.values())
if should_start != should_start_prev or (count == 0):
params.put_bool("IsOnroad", should_start)
params.put_bool("IsOffroad", not should_start)
HARDWARE.set_power_save(not should_start)
if should_start:
off_ts = None
if started_ts is None:
started_ts = sec_since_boot()
started_seen = True
else:
if startup_conditions["ignition"] and (startup_conditions != startup_conditions_prev):
cloudlog.event("Startup blocked", startup_conditions=startup_conditions)
started_ts = None
if off_ts is None:
off_ts = sec_since_boot()
# Offroad power monitoring
power_monitor.calculate(peripheralState, startup_conditions["ignition"])
msg.deviceState.offroadPowerUsageUwh = power_monitor.get_power_used()
msg.deviceState.carBatteryCapacityUwh = max(0, power_monitor.get_car_battery_capacity())
# Check if we need to disable charging (handled by boardd)
msg.deviceState.chargingDisabled = power_monitor.should_disable_charging(startup_conditions["ignition"], in_car, off_ts)
# Check if we need to shut down
if power_monitor.should_shutdown(peripheralState, startup_conditions["ignition"], in_car, off_ts, started_seen):
cloudlog.info(f"shutting device down, offroad since {off_ts}")
# TODO: add function for blocking cloudlog instead of sleep
time.sleep(10)
HARDWARE.shutdown()
# If UI has crashed, set the brightness to reasonable non-zero value
ui_running = "ui" in (p.name for p in sm["managerState"].processes if p.running)
if ui_running_prev and not ui_running:
HARDWARE.set_screen_brightness(20)
ui_running_prev = ui_running
msg.deviceState.chargingError = current_filter.x > 0. and msg.deviceState.batteryPercent < 90 # if current is positive, then battery is being discharged
msg.deviceState.started = started_ts is not None
msg.deviceState.startedMonoTime = int(1e9*(started_ts or 0))
last_ping = params.get("LastAthenaPingTime")
if last_ping is not None:
msg.deviceState.lastAthenaPingTime = int(last_ping)
msg.deviceState.thermalStatus = thermal_status
pm.send("deviceState", msg)
if EON and not is_uno:
set_offroad_alert_if_changed("Offroad_ChargeDisabled", (not usb_power))
should_start_prev = should_start
startup_conditions_prev = startup_conditions.copy()
# report to server once every 10 minutes
if (count % int(600. / DT_TRML)) == 0:
if EON and started_ts is None and msg.deviceState.memoryUsagePercent > 40:
cloudlog.event("High offroad memory usage", mem=msg.deviceState.memoryUsagePercent)
cloudlog.event("STATUS_PACKET",
count=count,
pandaStates=(strip_deprecated_keys(pandaStates.to_dict()) if pandaStates else None),
peripheralState=strip_deprecated_keys(peripheralState.to_dict()),
location=(strip_deprecated_keys(sm["gpsLocationExternal"].to_dict()) if sm.alive["gpsLocationExternal"] else None),
deviceState=strip_deprecated_keys(msg.to_dict()))
count += 1
def update(self, active, v_ego, angle_steers, steer_override, d_poly,
angle_offset, VM, PL):
cur_time = sec_since_boot()
self.mpc_updated = False
# TODO: this creates issues in replay when rewinding time: mpc won't run
if self.last_mpc_ts < PL.last_md_ts:
self.last_mpc_ts = PL.last_md_ts
self.angle_steers_des_prev = self.angle_steers_des_mpc
curvature_factor = VM.curvature_factor(v_ego)
l_poly = libmpc_py.ffi.new("double[4]", list(PL.PP.l_poly))
r_poly = libmpc_py.ffi.new("double[4]", list(PL.PP.r_poly))
p_poly = libmpc_py.ffi.new("double[4]", list(PL.PP.p_poly))
# account for actuation delay
self.cur_state = calc_states_after_delay(self.cur_state, v_ego,
angle_steers,
curvature_factor,
VM.CP.steerRatio,
VM.CP.steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution, l_poly,
r_poly, p_poly, PL.PP.l_prob, PL.PP.r_prob,
PL.PP.p_prob, curvature_factor, v_ego_mpc,
PL.PP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
else:
delta_desired = math.radians(angle_steers -
angle_offset) / VM.CP.steerRatio
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * VM.CP.steerRatio) + angle_offset)
self.angle_steers_des_time = cur_time
self.mpc_updated = True
# Check for infeasable MPC solution
self.mpc_nans = np.any(np.isnan(list(self.mpc_solution[0].delta)))
t = sec_since_boot()
if self.mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, VM.CP.steerRateCost)
self.cur_state[0].delta = math.radians(
angle_steers) / VM.CP.steerRatio
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if v_ego < 0.3 or not active:
output_steer = 0.0
self.pid.reset()
else:
# TODO: ideally we should interp, but for tuning reasons we keep the mpc solution
# constant for 0.05s.
#dt = min(cur_time - self.angle_steers_des_time, _DT_MPC + _DT) + _DT # no greater than dt mpc + dt, to prevent too high extraps
#self.angle_steers_des = self.angle_steers_des_prev + (dt / _DT_MPC) * (self.angle_steers_des_mpc - self.angle_steers_des_prev)
self.angle_steers_des = self.angle_steers_des_mpc
steers_max = get_steer_max(VM.CP, v_ego)
self.pid.pos_limit = steers_max
self.pid.neg_limit = -steers_max
steer_feedforward = self.angle_steers_des # feedforward desired angle
if VM.CP.steerControlType == car.CarParams.SteerControlType.torque:
steer_feedforward *= v_ego**2 # proportional to realigning tire momentum (~ lateral accel)
deadzone = 0.0
output_steer = self.pid.update(self.angle_steers_des,
angle_steers,
check_saturation=(v_ego > 10),
override=steer_override,
feedforward=steer_feedforward,
speed=v_ego,
deadzone=deadzone)
self.sat_flag = self.pid.saturated
return output_steer, float(self.angle_steers_des)
def update(self, sm, pm, CP, VM):
if not travis:
self.arne_sm.update(0)
gas_button_status = self.arne_sm['arne182Status'].gasbuttonstatus
if gas_button_status == 1:
self.blindspotwait = 10
elif gas_button_status == 2:
self.blindspotwait = 30
else:
self.blindspotwait = 20
if self.arne_sm['arne182Status'].rightBlindspot:
self.blindspotTrueCounterright = 0
else:
self.blindspotTrueCounterright = self.blindspotTrueCounterright + 1
if self.arne_sm['arne182Status'].leftBlindspot:
self.blindspotTrueCounterleft = 0
else:
self.blindspotTrueCounterleft = self.blindspotTrueCounterleft + 1
else:
self.blindspotwait = 10
self.blindspotTrueCounterleft = 0
self.blindspotTrueCounterright = 0
v_ego = sm['carState'].vEgo
angle_steers = sm['carState'].steeringAngle
active = sm['controlsState'].active
angle_offset = sm['liveParameters'].angleOffset
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
#VM.update_params(sm['liveParameters'].stiffnessFactor, sm['liveParameters'].steerRatio)
#VM.update_params(sm['liveParameters'].stiffnessFactor, CP.steerRatio)
VM.update_params(sm['liveParameters'].stiffnessFactor, self.steerRatio)
curvature_factor = VM.curvature_factor(v_ego)
# Get steerRatio and steerRateCost from kegman.json every x seconds
self.mpc_frame += 1
if self.mpc_frame % 500 == 0:
# live tuning through /data/openpilot/tune.py overrides interface.py settings
self.sR_Boost_new = interp(v_ego, self.sR_BoostBP, self.sR_Boost)
self.steerRatio = self.op_params.get('steer_ratio', default=12.0)
self.sR = [self.steerRatio, self.steerRatio + self.sR_Boost_new]
self.sR_time = 100
self.mpc_frame = 0
if v_ego > 5.0:
# boost steerRatio by boost amount if desired steer angle is high
self.steerRatio_new = interp(abs(angle_steers), self.sRBP, self.sR)
self.sR_delay_counter += 1
if self.sR_delay_counter % self.sR_time != 0:
if self.steerRatio_new > self.steerRatio:
self.steerRatio = self.steerRatio_new
else:
self.steerRatio = self.steerRatio_new
self.sR_delay_counter = 0
else:
self.steerRatio = self.sR[0]
print("steerRatio = ", self.steerRatio)
self.LP.parse_model(sm['model'])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < self.alca_min_speed * CV.MPH_TO_MS
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (
not one_blinker
) or (not self.lane_change_enabled) or (
sm['carState'].steeringPressed and
((sm['carState'].steeringTorque > 0
and self.lane_change_direction == LaneChangeDirection.right) or
(sm['carState'].steeringTorque < 0
and self.lane_change_direction == LaneChangeDirection.left))):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
if sm['carState'].leftBlinker:
lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
lane_change_direction = LaneChangeDirection.right
#if self.alca_nudge_required:
torque_applied = (sm['carState'].steeringPressed and \
((sm['carState'].steeringTorque > 0 and lane_change_direction == LaneChangeDirection.left and not sm['carState'].leftBlindspot) or \
(sm['carState'].steeringTorque < 0 and lane_change_direction == LaneChangeDirection.right and not sm['carState'].rightBlindspot))) or \
(not self.alca_nudge_required and self.blindspotTrueCounterleft > self.blindspotwait and lane_change_direction == LaneChangeDirection.left) or \
(not self.alca_nudge_required and self.blindspotTrueCounterright > self.blindspotwait and lane_change_direction == LaneChangeDirection.right)
#else:
# torque_applied = True
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
self.blindspotTrueCounterleft = 0
self.blindspotTrueCounterright = 0
self.lane_change_state = LaneChangeState.preLaneChange
self.lane_change_ll_prob = 1.0
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
self.blindspotTrueCounterleft = 0
self.blindspotTrueCounterright = 0
elif torque_applied:
self.lane_change_state = LaneChangeState.laneChangeStarting
# starting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .2s
self.lane_change_ll_prob = max(
self.lane_change_ll_prob - 2 * DT_MDL, 0.0)
# 98% certainty
if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01:
self.lane_change_state = LaneChangeState.laneChangeFinishing
if (self.arne_sm['arne182Status'].rightBlindspot
and lane_change_direction == LaneChangeDirection.right
) or (self.arne_sm['arne182Status'].leftBlindspot and
lane_change_direction == LaneChangeDirection.left):
self.lane_change_state = LaneChangeState.preLaneChange
self.blindspotTrueCounterleft = 0
self.blindspotTrueCounterright = 0
# finishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
# fade in laneline over 1s
self.lane_change_ll_prob = min(
self.lane_change_ll_prob + 0.5 * DT_MDL, 1.0)
if one_blinker and self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.preLaneChange
self.blindspotTrueCounterleft = 0
self.blindspotTrueCounterright = 0
elif self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.off
self.blindspotTrueCounterright = 0
self.blindspotTrueCounterleft = 0
if self.lane_change_state in [
LaneChangeState.off, LaneChangeState.preLaneChange
]:
self.lane_change_timer = 0.0
else:
self.lane_change_timer += DT_MDL
self.prev_one_blinker = one_blinker
desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Turn off lanes during lane change
if desire == log.PathPlan.Desire.laneChangeRight or desire == log.PathPlan.Desire.laneChangeLeft:
self.LP.l_prob *= self.lane_change_ll_prob
self.LP.r_prob *= self.lane_change_ll_prob
self.LP.update_d_poly(v_ego)
# account for actuation delay
self.cur_state = calc_states_after_delay(self.cur_state, v_ego,
angle_steers - angle_offset,
curvature_factor,
self.steerRatio,
CP.steerActuatorDelay)
#if abs(angle_steers - angle_offset) > 4 and CP.lateralTuning.which() == 'pid': #check if this causes laggy steering
# self.cur_state[0].delta = math.radians(angle_steers - angle_offset) / VM.sR
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly),
list(self.LP.d_poly), self.LP.l_prob,
self.LP.r_prob, curvature_factor, v_ego_mpc,
self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
rate_desired = math.degrees(self.mpc_solution[0].rate[0] *
self.steerRatio)
else:
delta_desired = math.radians(angle_steers -
angle_offset) / self.steerRatio
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * self.steerRatio) + angle_offset)
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, CP.steerRateCost)
self.cur_state[0].delta = math.radians(
angle_steers - angle_offset) / self.steerRatio
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=[
'carState', 'controlsState', 'liveParameters', 'model'
])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
plan_send.pathPlan.angleOffset = float(
sm['liveParameters'].angleOffsetAverage)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.sensorValid = bool(sm['liveParameters'].sensorValid)
plan_send.pathPlan.posenetValid = bool(
sm['liveParameters'].posenetValid) or self.posenetValid
self.posenetValid = bool(sm['liveParameters'].posenetValid)
plan_send.pathPlan.desire = desire
plan_send.pathPlan.laneChangeState = self.lane_change_state
plan_send.pathPlan.laneChangeDirection = self.lane_change_direction
pm.send('pathPlan', plan_send)
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
msg = messaging_arne.new_message('latControl')
msg.latControl.anglelater = math.degrees(
list(self.mpc_solution[0].delta)[-1])
if not travis:
self.arne_pm.send('latControl', msg)
def manager_thread():
cloudlog.info("manager start")
cloudlog.info({"environ": os.environ})
# save boot log
subprocess.call("./bootlog",
cwd=os.path.join(BASEDIR, "selfdrive/loggerd"))
params = Params()
ignore = []
if params.get("DongleId", encoding='utf8') == UNREGISTERED_DONGLE_ID:
ignore += ["manage_athenad", "uploader"]
if os.getenv("NOBOARD") is not None:
ignore.append("pandad")
if os.getenv("BLOCK") is not None:
ignore += os.getenv("BLOCK").split(",")
ensure_running(managed_processes.values(), started=False, not_run=ignore)
started_prev = False
sm = messaging.SubMaster(['deviceState'])
pm = messaging.PubMaster(['managerState'])
while True:
sm.update()
not_run = ignore[:]
if sm['deviceState'].freeSpacePercent < 5:
not_run.append("loggerd")
started = sm['deviceState'].started
driverview = params.get_bool("IsDriverViewEnabled")
ensure_running(managed_processes.values(), started, driverview,
not_run)
# trigger an update after going offroad
if started_prev and not started and 'updated' in managed_processes:
os.sync()
managed_processes['updated'].signal(signal.SIGHUP)
started_prev = started
running = ' '.join([
"%s%s\u001b[0m" %
("\u001b[32m" if p.proc.is_alive() else "\u001b[31m", p.name)
for p in managed_processes.values() if p.proc
])
print(running)
cloudlog.debug(running)
# send managerState
msg = messaging.new_message('managerState')
msg.managerState.processes = [
p.get_process_state_msg() for p in managed_processes.values()
]
pm.send('managerState', msg)
# Exit main loop when uninstall/shutdown/reboot is needed
shutdown = False
for param in ("DoUninstall", "DoShutdown", "DoReboot"):
if params.get_bool(param):
cloudlog.warning(f"Shutting down manager - {param} set")
shutdown = True
if shutdown:
break
def register(show_spinner=False) -> str:
params = Params()
params.put("SubscriberInfo", HARDWARE.get_subscriber_info())
IMEI = params.get("IMEI", encoding='utf8')
HardwareSerial = params.get("HardwareSerial", encoding='utf8')
dongle_id = params.get("DongleId", encoding='utf8')
needs_registration = None in (IMEI, HardwareSerial, dongle_id)
pubkey = Path(PERSIST + "/comma/id_rsa.pub")
if not pubkey.is_file():
dongle_id = UNREGISTERED_DONGLE_ID
cloudlog.warning(f"missing public key: {pubkey}")
elif needs_registration:
if show_spinner:
spinner = Spinner()
spinner.update("registering device")
# Create registration token, in the future, this key will make JWTs directly
with open(PERSIST +
"/comma/id_rsa.pub") as f1, open(PERSIST +
"/comma/id_rsa") as f2:
public_key = f1.read()
private_key = f2.read()
# Block until we get the imei
serial = HARDWARE.get_serial()
start_time = time.monotonic()
imei1, imei2 = None, None
while imei1 is None and imei2 is None:
try:
imei1, imei2 = HARDWARE.get_imei(0), HARDWARE.get_imei(1)
except Exception:
cloudlog.exception("Error getting imei, trying again...")
time.sleep(1)
if time.monotonic() - start_time > 60 and show_spinner:
spinner.update(
f"registering device - serial: {serial}, IMEI: ({imei1}, {imei2})"
)
params.put("IMEI", imei1)
params.put("HardwareSerial", serial)
backoff = 0
start_time = time.monotonic()
while True:
try:
register_token = jwt.encode(
{
'register': True,
'exp': datetime.utcnow() + timedelta(hours=1)
},
private_key,
algorithm='RS256')
cloudlog.info("getting pilotauth")
resp = api_get("v2/pilotauth/",
method='POST',
timeout=15,
imei=imei1,
imei2=imei2,
serial=serial,
public_key=public_key,
register_token=register_token)
if resp.status_code in (402, 403):
cloudlog.info(
f"Unable to register device, got {resp.status_code}")
dongle_id = UNREGISTERED_DONGLE_ID
else:
dongleauth = json.loads(resp.text)
dongle_id = dongleauth["dongle_id"]
break
except Exception:
cloudlog.exception("failed to authenticate")
backoff = min(backoff + 1, 15)
time.sleep(backoff)
if time.monotonic() - start_time > 60 and show_spinner:
spinner.update(
f"registering device - serial: {serial}, IMEI: ({imei1}, {imei2})"
)
if show_spinner:
spinner.close()
if dongle_id:
params.put("DongleId", dongle_id)
set_offroad_alert("Offroad_UnofficialHardware",
dongle_id == UNREGISTERED_DONGLE_ID)
return dongle_id
def uninstall():
cloudlog.warning("uninstalling")
with open('/cache/recovery/command', 'w') as f:
f.write('--wipe_data\n')
# IPowerManager.reboot(confirm=false, reason="recovery", wait=true)
HARDWARE.reboot(reason="recovery")
def manager_thread():
# now loop
context = zmq.Context()
thermal_sock = messaging.pub_sock(context, service_list['thermal'].port)
health_sock = messaging.sub_sock(context, service_list['health'].port)
location_sock = messaging.sub_sock(context,
service_list['gpsLocation'].port)
cloudlog.info("manager start")
cloudlog.info({"environ": os.environ})
for p in persistent_processes:
start_managed_process(p)
# start frame
system("am start -n ai.comma.plus.frame/.MainActivity")
# do this before panda flashing
setup_eon_fan()
if os.getenv("NOBOARD") is None:
start_managed_process("pandad")
params = Params()
passive_starter = LocationStarter()
started_ts = None
off_ts = None
logger_dead = False
count = 0
fan_speed = 0
ignition_seen = False
started_seen = False
panda_seen = False
health_sock.RCVTIMEO = 1500
while 1:
# get health of board, log this in "thermal"
td = messaging.recv_sock(health_sock, wait=True)
location = messaging.recv_sock(location_sock)
location = location.gpsLocation if location else None
print td
# replace thermald
msg = read_thermal()
# loggerd is gated based on free space
statvfs = os.statvfs(ROOT)
avail = (statvfs.f_bavail * 1.0) / statvfs.f_blocks
# thermal message now also includes free space
msg.thermal.freeSpace = avail
with open("/sys/class/power_supply/battery/capacity") as f:
msg.thermal.batteryPercent = int(f.read())
with open("/sys/class/power_supply/battery/status") as f:
msg.thermal.batteryStatus = f.read().strip()
with open("/sys/class/power_supply/usb/online") as f:
msg.thermal.usbOnline = bool(int(f.read()))
# TODO: add car battery voltage check
max_temp = max(msg.thermal.cpu0, msg.thermal.cpu1, msg.thermal.cpu2,
msg.thermal.cpu3) / 10.0
bat_temp = msg.thermal.bat / 1000.
fan_speed = handle_fan(max_temp, bat_temp, fan_speed)
msg.thermal.fanSpeed = fan_speed
msg.thermal.started = started_ts is not None
msg.thermal.startedTs = int(1e9 * (started_ts or 0))
thermal_sock.send(msg.to_bytes())
print msg
# uploader is gated based on the phone temperature
if max_temp > 85.0:
cloudlog.warning("over temp: %r", max_temp)
kill_managed_process("uploader")
elif max_temp < 70.0:
start_managed_process("uploader")
if avail < 0.05:
logger_dead = True
# start constellation of processes when the car starts
ignition = td is not None and td.health.started
ignition_seen = ignition_seen or ignition
# add voltage check for ignition
if not ignition_seen and td is not None and td.health.voltage > 13500:
ignition = True
do_uninstall = params.get("DoUninstall") == "1"
accepted_terms = params.get("HasAcceptedTerms") == "1"
completed_training = params.get(
"CompletedTrainingVersion") == training_version
should_start = ignition
# have we seen a panda?
panda_seen = panda_seen or td is not None
# start on gps movement if we haven't seen ignition and are in passive mode
should_start = should_start or (
not (ignition_seen and td) # seen ignition and panda is connected
and params.get("Passive") == "1"
and passive_starter.update(started_ts, location))
# with 2% left, we killall, otherwise the phone will take a long time to boot
should_start = should_start and avail > 0.02
# require usb power
should_start = should_start and msg.thermal.usbOnline
should_start = should_start and accepted_terms and completed_training and (
not do_uninstall)
# if any CPU gets above 107 or the battery gets above 63, kill all processes
# controls will warn with CPU above 95 or battery above 60
if max_temp > 107.0 or msg.thermal.bat >= 63000:
# TODO: Add a better warning when this is happening
should_start = False
if should_start:
off_ts = None
if started_ts is None:
params.car_start()
started_ts = sec_since_boot()
started_seen = True
for p in car_started_processes:
if p == "loggerd" and logger_dead:
kill_managed_process(p)
else:
start_managed_process(p)
else:
started_ts = None
if off_ts is None:
off_ts = sec_since_boot()
logger_dead = False
for p in car_started_processes:
kill_managed_process(p)
# shutdown if the battery gets lower than 3%, t's discharging, we aren't running for
# more than a minute but we were running
if msg.thermal.batteryPercent < 3 and msg.thermal.batteryStatus == "Discharging" and \
started_seen and (sec_since_boot() - off_ts) > 60:
os.system('LD_LIBRARY_PATH="" svc power shutdown')
# check the status of all processes, did any of them die?
for p in running:
cloudlog.debug(" running %s %s" % (p, running[p]))
# report to server once per minute
if (count % 60) == 0:
cloudlog.event("STATUS_PACKET",
running=running.keys(),
count=count,
health=(td.to_dict() if td else None),
location=(location.to_dict() if location else None),
thermal=msg.to_dict())
if do_uninstall:
break
count += 1
def get_params(candidate,
fingerprint=gen_empty_fingerprint(),
has_relay=False,
car_fw=[]):
ret = CarInterfaceBase.get_std_params(candidate, fingerprint,
has_relay)
ret.carName = "toyota"
ret.safetyModel = car.CarParams.SafetyModel.toyota
ret.steerActuatorDelay = 0.12 # Default delay, Prius has larger delay
ret.steerLimitTimer = 0.4
if candidate not in [CAR.PRIUS, CAR.RAV4,
CAR.RAV4H]: # These cars use LQR/INDI
ret.lateralTuning.init('pid')
ret.lateralTuning.pid.kiBP, ret.lateralTuning.pid.kpBP = [[0.],
[0.]]
if candidate == CAR.PRIUS:
stop_and_go = True
ret.safetyParam = 66 # see conversion factor for STEER_TORQUE_EPS in dbc file
ret.wheelbase = 2.70
ret.steerRatio = 15.74 # unknown end-to-end spec
tire_stiffness_factor = 0.6371 # hand-tune
ret.mass = 3045. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.init('indi')
ret.lateralTuning.indi.innerLoopGain = 4.0
ret.lateralTuning.indi.outerLoopGain = 3.0
ret.lateralTuning.indi.timeConstant = 1.0
ret.lateralTuning.indi.actuatorEffectiveness = 1.0
ret.steerActuatorDelay = 0.5
elif candidate in [CAR.RAV4, CAR.RAV4H]:
stop_and_go = True if (candidate in CAR.RAV4H) else False
ret.safetyParam = 73
ret.wheelbase = 2.65
ret.steerRatio = 16.88 # 14.5 is spec end-to-end
tire_stiffness_factor = 0.5533
ret.mass = 3650. * CV.LB_TO_KG + STD_CARGO_KG # mean between normal and hybrid
ret.lateralTuning.init('lqr')
ret.lateralTuning.lqr.scale = 1500.0
ret.lateralTuning.lqr.ki = 0.05
ret.lateralTuning.lqr.a = [0., 1., -0.22619643, 1.21822268]
ret.lateralTuning.lqr.b = [-1.92006585e-04, 3.95603032e-05]
ret.lateralTuning.lqr.c = [1., 0.]
ret.lateralTuning.lqr.k = [-110.73572306, 451.22718255]
ret.lateralTuning.lqr.l = [0.3233671, 0.3185757]
ret.lateralTuning.lqr.dcGain = 0.002237852961363602
elif candidate == CAR.COROLLA:
stop_and_go = False
ret.safetyParam = 100
ret.wheelbase = 2.70
ret.steerRatio = 18.27
tire_stiffness_factor = 0.444 # not optimized yet
ret.mass = 2860. * CV.LB_TO_KG + STD_CARGO_KG # mean between normal and hybrid
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.2],
[0.05]]
ret.lateralTuning.pid.kf = 0.00003 # full torque for 20 deg at 80mph means 0.00007818594
elif candidate == CAR.LEXUS_RX:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.79
ret.steerRatio = 14.8
tire_stiffness_factor = 0.5533
ret.mass = 4387. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.05]]
ret.lateralTuning.pid.kf = 0.00006
elif candidate == CAR.LEXUS_RXH:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.79
ret.steerRatio = 16. # 14.8 is spec end-to-end
tire_stiffness_factor = 0.444 # not optimized yet
ret.mass = 4481. * CV.LB_TO_KG + STD_CARGO_KG # mean between min and max
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.1]]
ret.lateralTuning.pid.kf = 0.00006 # full torque for 10 deg at 80mph means 0.00007818594
elif candidate == CAR.LEXUS_RX_TSS2:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.79
ret.steerRatio = 14.8
tire_stiffness_factor = 0.5533 # not optimized yet
ret.mass = 4387. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.1]]
ret.lateralTuning.pid.kf = 0.00007818594
elif candidate == CAR.LEXUS_RXH_TSS2:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.79
ret.steerRatio = 16.0 # 14.8 is spec end-to-end
tire_stiffness_factor = 0.444 # not optimized yet
ret.mass = 4481.0 * CV.LB_TO_KG + STD_CARGO_KG # mean between min and max
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.15]]
ret.lateralTuning.pid.kf = 0.00007818594
elif candidate in [CAR.CHR, CAR.CHRH]:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.63906
ret.steerRatio = 13.6
tire_stiffness_factor = 0.7933
ret.mass = 3300. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.723],
[0.0428]]
ret.lateralTuning.pid.kf = 0.00006
elif candidate in [CAR.CAMRY, CAR.CAMRYH]:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.82448
ret.steerRatio = 13.7
tire_stiffness_factor = 0.7933
ret.mass = 3400. * CV.LB_TO_KG + STD_CARGO_KG # mean between normal and hybrid
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.1]]
ret.lateralTuning.pid.kf = 0.00006
elif candidate in [CAR.HIGHLANDER_TSS2, CAR.HIGHLANDERH_TSS2]:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.84988 # 112.2 in = 2.84988 m
ret.steerRatio = 16.0
tire_stiffness_factor = 0.8
ret.mass = 4700. * CV.LB_TO_KG + STD_CARGO_KG # 4260 + 4-5 people
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[
0.18
], [0.015]] # community tuning
ret.lateralTuning.pid.kf = 0.00012 # community tuning
elif candidate in [CAR.HIGHLANDER, CAR.HIGHLANDERH]:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.78
ret.steerRatio = 16.0
tire_stiffness_factor = 0.8
ret.mass = 4607. * CV.LB_TO_KG + STD_CARGO_KG # mean between normal and hybrid limited
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[
0.18
], [0.015]] # community tuning
ret.lateralTuning.pid.kf = 0.00012 # community tuning
elif candidate == CAR.AVALON:
stop_and_go = False
ret.safetyParam = 73
ret.wheelbase = 2.82
ret.steerRatio = 14.8 # Found at https://pressroom.toyota.com/releases/2016+avalon+product+specs.download
tire_stiffness_factor = 0.7983
ret.mass = 3505. * CV.LB_TO_KG + STD_CARGO_KG # mean between normal and hybrid
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.17],
[0.03]]
ret.lateralTuning.pid.kf = 0.00006
elif candidate == CAR.RAV4_TSS2:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.68986
ret.steerRatio = 14.3
tire_stiffness_factor = 0.7933
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.15],
[0.05]]
ret.mass = 3370. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kf = 0.00004
for fw in car_fw:
if fw.ecu == "eps" and fw.fwVersion == b"8965B42170\x00\x00\x00\x00\x00\x00":
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[
0.6
], [0.1]]
ret.lateralTuning.pid.kf = 0.00007818594
break
elif candidate == CAR.RAV4H_TSS2:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.68986
ret.steerRatio = 14.3
tire_stiffness_factor = 0.7933
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.15],
[0.05]]
ret.mass = 3800. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kf = 0.00004
for fw in car_fw:
if fw.ecu == "eps" and fw.fwVersion == b"8965B42170\x00\x00\x00\x00\x00\x00":
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[
0.6
], [0.1]]
ret.lateralTuning.pid.kf = 0.00007818594
break
elif candidate in [CAR.COROLLA_TSS2, CAR.COROLLAH_TSS2]:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.63906
ret.steerRatio = 13.9
tire_stiffness_factor = 0.444 # not optimized yet
ret.mass = 3060. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.1]]
ret.lateralTuning.pid.kf = 0.00007818594
elif candidate in [CAR.LEXUS_ES_TSS2, CAR.LEXUS_ESH_TSS2]:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.8702
ret.steerRatio = 16.0 # not optimized
tire_stiffness_factor = 0.444 # not optimized yet
ret.mass = 3704. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.1]]
ret.lateralTuning.pid.kf = 0.00007818594
elif candidate == CAR.SIENNA:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 3.03
ret.steerRatio = 15.5
tire_stiffness_factor = 0.444
ret.mass = 4590. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.19],
[0.02]]
ret.lateralTuning.pid.kf = 0.00007818594
elif candidate == CAR.LEXUS_IS:
stop_and_go = False
ret.safetyParam = 77
ret.wheelbase = 2.79908
ret.steerRatio = 13.3
tire_stiffness_factor = 0.444
ret.mass = 3736.8 * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.3],
[0.05]]
ret.lateralTuning.pid.kf = 0.00006
elif candidate == CAR.LEXUS_CTH:
stop_and_go = True
ret.safetyParam = 100
ret.wheelbase = 2.60
ret.steerRatio = 18.6
tire_stiffness_factor = 0.517
ret.mass = 3108 * CV.LB_TO_KG + STD_CARGO_KG # mean between min and max
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.3],
[0.05]]
ret.lateralTuning.pid.kf = 0.00007
elif candidate == CAR.LEXUS_NXH:
stop_and_go = True
ret.safetyParam = 73
ret.wheelbase = 2.66
ret.steerRatio = 14.7
tire_stiffness_factor = 0.444 # not optimized yet
ret.mass = 4070 * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.1]]
ret.lateralTuning.pid.kf = 0.00006
ret.steerRateCost = 1.
ret.centerToFront = ret.wheelbase * 0.44
# TODO: get actual value, for now starting with reasonable value for
# civic and scaling by mass and wheelbase
ret.rotationalInertia = scale_rot_inertia(ret.mass, ret.wheelbase)
# TODO: start from empirically derived lateral slip stiffness for the civic and scale by
# mass and CG position, so all cars will have approximately similar dyn behaviors
ret.tireStiffnessFront, ret.tireStiffnessRear = scale_tire_stiffness(
ret.mass,
ret.wheelbase,
ret.centerToFront,
tire_stiffness_factor=tire_stiffness_factor)
ret.enableCamera = is_ecu_disconnected(fingerprint[0], FINGERPRINTS,
ECU_FINGERPRINT, candidate,
Ecu.fwdCamera) or has_relay
# Detect smartDSU, which intercepts ACC_CMD from the DSU allowing openpilot to send it
smartDsu = 0x2FF in fingerprint[0]
# In TSS2 cars the camera does long control
ret.enableDsu = is_ecu_disconnected(
fingerprint[0], FINGERPRINTS, ECU_FINGERPRINT, candidate,
Ecu.dsu) and candidate not in TSS2_CAR
ret.enableGasInterceptor = 0x201 in fingerprint[0]
# if the smartDSU is detected, openpilot can send ACC_CMD (and the smartDSU will block it from the DSU) or not (the DSU is "connected")
ret.openpilotLongitudinalControl = ret.enableCamera and (
smartDsu or ret.enableDsu or candidate in TSS2_CAR)
cloudlog.warning("ECU Camera Simulated: %r", ret.enableCamera)
cloudlog.warning("ECU DSU Simulated: %r", ret.enableDsu)
cloudlog.warning("ECU Gas Interceptor: %r", ret.enableGasInterceptor)
# min speed to enable ACC. if car can do stop and go, then set enabling speed
# to a negative value, so it won't matter.
ret.minEnableSpeed = -1. if (
stop_and_go or ret.enableGasInterceptor) else 19. * CV.MPH_TO_MS
# removing the DSU disables AEB and it's considered a community maintained feature
# intercepting the DSU is a community feature since it requires unofficial hardware
ret.communityFeature = ret.enableGasInterceptor or ret.enableDsu or smartDsu
ret.longitudinalTuning.deadzoneBP = [0., 9.]
ret.longitudinalTuning.deadzoneV = [0., .15]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kiBP = [0., 35.]
if ret.enableGasInterceptor:
ret.gasMaxBP = [0., 9., 35]
ret.gasMaxV = [0.2, 0.5, 0.7]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiV = [0.18, 0.12]
else:
ret.gasMaxBP = [0.]
ret.gasMaxV = [0.5]
ret.longitudinalTuning.kpV = [3.6, 2.4, 1.5]
ret.longitudinalTuning.kiV = [0.54, 0.36]
return ret
def get_params(candidate,
fingerprint=gen_empty_fingerprint(),
has_relay=False,
car_fw=[]): # pylint: disable=dangerous-default-value
ret = CarInterfaceBase.get_std_params(candidate, fingerprint,
has_relay)
ret.carName = "honda"
if candidate in HONDA_BOSCH:
ret.safetyModel = car.CarParams.SafetyModel.hondaBoschHarness if has_relay else car.CarParams.SafetyModel.hondaBoschGiraffe
rdr_bus = 0 if has_relay else 2
ret.enableCamera = is_ecu_disconnected(
fingerprint[rdr_bus], FINGERPRINTS, ECU_FINGERPRINT, candidate,
Ecu.fwdCamera) or has_relay
ret.radarOffCan = True
ret.openpilotLongitudinalControl = False
else:
ret.safetyModel = car.CarParams.SafetyModel.hondaNidec
ret.enableCamera = is_ecu_disconnected(
fingerprint[0], FINGERPRINTS, ECU_FINGERPRINT, candidate,
Ecu.fwdCamera) or has_relay
ret.enableGasInterceptor = 0x201 in fingerprint[0]
ret.openpilotLongitudinalControl = ret.enableCamera
cloudlog.warning("ECU Camera Simulated: %r", ret.enableCamera)
cloudlog.warning("ECU Gas Interceptor: %r", ret.enableGasInterceptor)
ret.enableCruise = not ret.enableGasInterceptor
ret.communityFeature = ret.enableGasInterceptor
# Certain Hondas have an extra steering sensor at the bottom of the steering rack,
# which improves controls quality as it removes the steering column torsion from feedback.
# Tire stiffness factor fictitiously lower if it includes the steering column torsion effect.
# For modeling details, see p.198-200 in "The Science of Vehicle Dynamics (2014), M. Guiggiani"
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0], [0]]
ret.lateralTuning.pid.kiBP, ret.lateralTuning.pid.kpBP = [[0.], [0.]]
ret.lateralTuning.pid.kf = 0.00006 # conservative feed-forward
eps_modified = False
for fw in car_fw:
if fw.ecu == "eps" and b"," in fw.fwVersion:
eps_modified = True
if candidate == CAR.CIVIC:
stop_and_go = True
ret.mass = CivicParams.MASS
ret.wheelbase = CivicParams.WHEELBASE
ret.centerToFront = CivicParams.CENTER_TO_FRONT
ret.steerRatio = 15.38 # 10.93 is end-to-end spec
if eps_modified:
# stock request input values: 0x0000, 0x00DE, 0x014D, 0x01EF, 0x0290, 0x0377, 0x0454, 0x0610, 0x06EE
# stock request output values: 0x0000, 0x0917, 0x0DC5, 0x1017, 0x119F, 0x140B, 0x1680, 0x1680, 0x1680
# modified request output values: 0x0000, 0x0917, 0x0DC5, 0x1017, 0x119F, 0x140B, 0x1680, 0x2880, 0x3180
# stock filter output values: 0x009F, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108
# modified filter output values: 0x009F, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0108, 0x0400, 0x0480
# note: max request allowed is 4096, but request is capped at 3840 in firmware, so modifications result in 2x max
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[
0, 2560, 8000
], [0, 2560, 3840]]
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.3],
[0.1]]
else:
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[
0, 2560
], [0, 2560]]
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[1.1],
[0.33]]
tire_stiffness_factor = 1.
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [3.6, 2.4, 1.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.54, 0.36]
elif candidate in (CAR.CIVIC_BOSCH, CAR.CIVIC_BOSCH_DIESEL):
stop_and_go = True
ret.mass = CivicParams.MASS
ret.wheelbase = CivicParams.WHEELBASE
ret.centerToFront = CivicParams.CENTER_TO_FRONT
ret.steerRatio = 15.38 # 10.93 is end-to-end spec
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 4096], [0, 4096]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 1.
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.8],
[0.24]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate in (CAR.ACCORD, CAR.ACCORD_15, CAR.ACCORDH):
stop_and_go = True
if not candidate == CAR.ACCORDH: # Hybrid uses same brake msg as hatch
ret.safetyParam = 1 # Accord and CRV 5G use an alternate user brake msg
ret.mass = 3279. * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 2.83
ret.centerToFront = ret.wheelbase * 0.39
ret.steerRatio = 16.33 # 11.82 is spec end-to-end
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 4096], [0, 4096]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.8467
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
if eps_modified:
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.3],
[0.09]]
else:
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.18]]
elif candidate == CAR.ACURA_ILX:
stop_and_go = False
ret.mass = 3095. * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 2.67
ret.centerToFront = ret.wheelbase * 0.37
ret.steerRatio = 18.61 # 15.3 is spec end-to-end
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 3840], [0, 3840]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.72
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.8],
[0.24]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate in (CAR.CRV, CAR.CRV_EU):
stop_and_go = False
ret.mass = 3572. * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 2.62
ret.centerToFront = ret.wheelbase * 0.41
ret.steerRatio = 16.89 # as spec
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 1000], [0, 1000]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.444
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.8],
[0.24]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate == CAR.CRV_5G:
stop_and_go = True
ret.safetyParam = 1 # Accord and CRV 5G use an alternate user brake msg
ret.mass = 3410. * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 2.66
ret.centerToFront = ret.wheelbase * 0.41
ret.steerRatio = 16.0 # 12.3 is spec end-to-end
if eps_modified:
# stock request input values: 0x0000, 0x00DB, 0x01BB, 0x0296, 0x0377, 0x0454, 0x0532, 0x0610, 0x067F
# stock request output values: 0x0000, 0x0500, 0x0A15, 0x0E6D, 0x1100, 0x1200, 0x129A, 0x134D, 0x1400
# modified request output values: 0x0000, 0x0500, 0x0A15, 0x0E6D, 0x1100, 0x1200, 0x1ACD, 0x239A, 0x2800
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[
0, 2560, 10000
], [0, 2560, 3840]]
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.3],
[0.1]]
else:
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[
0, 3840
], [0, 3840]]
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[
0.64
], [0.192]]
tire_stiffness_factor = 0.677
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate == CAR.CRV_HYBRID:
stop_and_go = True
ret.safetyParam = 1 # Accord and CRV 5G use an alternate user brake msg
ret.mass = 1667. + STD_CARGO_KG # mean of 4 models in kg
ret.wheelbase = 2.66
ret.centerToFront = ret.wheelbase * 0.41
ret.steerRatio = 16.0 # 12.3 is spec end-to-end
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 4096], [0, 4096]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.677
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.18]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate == CAR.FIT:
stop_and_go = False
ret.mass = 2644. * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 2.53
ret.centerToFront = ret.wheelbase * 0.39
ret.steerRatio = 13.06
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 4096], [0, 4096]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.75
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.25],
[0.06]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate == CAR.HRV:
stop_and_go = False
ret.mass = 3125 * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 2.61
ret.centerToFront = ret.wheelbase * 0.41
ret.steerRatio = 15.2
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [[0, 4096],
[0, 4096]]
tire_stiffness_factor = 0.5
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.16],
[0.025]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate == CAR.ACURA_RDX:
stop_and_go = False
ret.mass = 3935. * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 2.68
ret.centerToFront = ret.wheelbase * 0.38
ret.steerRatio = 15.0 # as spec
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 1000], [0, 1000]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.444
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.8],
[0.24]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate == CAR.ODYSSEY:
stop_and_go = False
ret.mass = 4471. * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 3.00
ret.centerToFront = ret.wheelbase * 0.41
ret.steerRatio = 14.35 # as spec
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 4096], [0, 4096]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.82
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.45],
[0.135]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate == CAR.ODYSSEY_CHN:
stop_and_go = False
ret.mass = 1849.2 + STD_CARGO_KG # mean of 4 models in kg
ret.wheelbase = 2.90
ret.centerToFront = ret.wheelbase * 0.41 # from CAR.ODYSSEY
ret.steerRatio = 14.35
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 32767], [0, 32767]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.82
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.45],
[0.135]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate in (CAR.PILOT, CAR.PILOT_2019):
stop_and_go = False
ret.mass = 4204. * CV.LB_TO_KG + STD_CARGO_KG # average weight
ret.wheelbase = 2.82
ret.centerToFront = ret.wheelbase * 0.428
ret.steerRatio = 17.25 # as spec
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 4096], [0, 4096]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.444
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.38],
[0.11]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate == CAR.RIDGELINE:
stop_and_go = False
ret.mass = 4515. * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 3.18
ret.centerToFront = ret.wheelbase * 0.41
ret.steerRatio = 15.59 # as spec
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 4096], [0, 4096]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.444
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.38],
[0.11]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
elif candidate == CAR.INSIGHT:
stop_and_go = True
ret.mass = 2987. * CV.LB_TO_KG + STD_CARGO_KG
ret.wheelbase = 2.7
ret.centerToFront = ret.wheelbase * 0.39
ret.steerRatio = 15.0 # 12.58 is spec end-to-end
ret.lateralParams.torqueBP, ret.lateralParams.torqueV = [
[0, 4096], [0, 4096]
] # TODO: determine if there is a dead zone at the top end
tire_stiffness_factor = 0.82
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.6],
[0.18]]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [1.2, 0.8, 0.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.18, 0.12]
else:
raise ValueError("unsupported car %s" % candidate)
# min speed to enable ACC. if car can do stop and go, then set enabling speed
# to a negative value, so it won't matter. Otherwise, add 0.5 mph margin to not
# conflict with PCM acc
ret.minEnableSpeed = -1. if (
stop_and_go or ret.enableGasInterceptor) else 25.5 * CV.MPH_TO_MS
# TODO: get actual value, for now starting with reasonable value for
# civic and scaling by mass and wheelbase
ret.rotationalInertia = scale_rot_inertia(ret.mass, ret.wheelbase)
# TODO: start from empirically derived lateral slip stiffness for the civic and scale by
# mass and CG position, so all cars will have approximately similar dyn behaviors
ret.tireStiffnessFront, ret.tireStiffnessRear = scale_tire_stiffness(
ret.mass,
ret.wheelbase,
ret.centerToFront,
tire_stiffness_factor=tire_stiffness_factor)
ret.gasMaxBP = [0.] # m/s
ret.gasMaxV = [0.6] if ret.enableGasInterceptor else [
0.
] # max gas allowed
ret.brakeMaxBP = [5., 20.] # m/s
ret.brakeMaxV = [1., 0.8] # max brake allowed
ret.stoppingControl = True
ret.startAccel = 0.5
ret.steerActuatorDelay = 0.1
ret.steerRateCost = 0.5
ret.steerLimitTimer = 0.8
return ret
def get_params(candidate,
fingerprint=gen_empty_fingerprint(),
vin="",
has_relay=False):
ret = car.CarParams.new_message()
ret.carName = "ford"
ret.carFingerprint = candidate
ret.carVin = vin
ret.isPandaBlack = has_relay
ret.safetyModel = car.CarParams.SafetyModel.ford
ret.dashcamOnly = True
# pedal
ret.enableCruise = True
ret.wheelbase = 2.85
ret.steerRatio = 14.8
ret.mass = 3045. * CV.LB_TO_KG + STD_CARGO_KG
ret.lateralTuning.pid.kiBP, ret.lateralTuning.pid.kpBP = [[0.], [0.]]
ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.01], [
0.005
]] # TODO: tune this
ret.lateralTuning.pid.kf = 1. / MAX_ANGLE # MAX Steer angle to normalize FF
ret.steerActuatorDelay = 0.1 # Default delay, not measured yet
ret.steerLimitTimer = 0.8
ret.steerRateCost = 1.0
ret.centerToFront = ret.wheelbase * 0.44
tire_stiffness_factor = 0.5328
# min speed to enable ACC. if car can do stop and go, then set enabling speed
# to a negative value, so it won't matter.
ret.minEnableSpeed = -1.
# TODO: get actual value, for now starting with reasonable value for
# civic and scaling by mass and wheelbase
ret.rotationalInertia = scale_rot_inertia(ret.mass, ret.wheelbase)
# TODO: start from empirically derived lateral slip stiffness for the civic and scale by
# mass and CG position, so all cars will have approximately similar dyn behaviors
ret.tireStiffnessFront, ret.tireStiffnessRear = scale_tire_stiffness(
ret.mass,
ret.wheelbase,
ret.centerToFront,
tire_stiffness_factor=tire_stiffness_factor)
# no rear steering, at least on the listed cars above
ret.steerRatioRear = 0.
ret.steerControlType = car.CarParams.SteerControlType.angle
# steer, gas, brake limitations VS speed
ret.steerMaxBP = [0.] # breakpoints at 1 and 40 kph
ret.steerMaxV = [1.0] # 2/3rd torque allowed above 45 kph
ret.gasMaxBP = [0.]
ret.gasMaxV = [0.5]
ret.brakeMaxBP = [5., 20.]
ret.brakeMaxV = [1., 0.8]
ret.enableCamera = is_ecu_disconnected(fingerprint[0], FINGERPRINTS,
ECU_FINGERPRINT, candidate,
ECU.CAM) or has_relay
ret.openpilotLongitudinalControl = False
cloudlog.warning("ECU Camera Simulated: %r", ret.enableCamera)
ret.stoppingControl = False
ret.startAccel = 0.0
ret.longitudinalTuning.deadzoneBP = [0., 9.]
ret.longitudinalTuning.deadzoneV = [0., .15]
ret.longitudinalTuning.kpBP = [0., 5., 35.]
ret.longitudinalTuning.kpV = [3.6, 2.4, 1.5]
ret.longitudinalTuning.kiBP = [0., 35.]
ret.longitudinalTuning.kiV = [0.54, 0.36]
return ret
def update(self, sm, CP):
self.use_lanelines = Params().get('EndToEndToggle') != b'1'
self.laneless_mode = int(Params().get("LanelessMode", encoding='utf8'))
self.v_cruise_kph = sm['controlsState'].vCruise
self.stand_still = sm['carState'].standStill
try:
lateral_control_method = 0
lateral_control_method = int(
sm['controlsState'].lateralControlMethod)
if lateral_control_method == 0:
self.output_scale = sm[
'controlsState'].lateralControlState.pidState.output
elif lateral_control_method == 1:
self.output_scale = sm[
'controlsState'].lateralControlState.indiState.output
elif lateral_control_method == 2:
self.output_scale = sm[
'controlsState'].lateralControlState.lqrState.output
except:
pass
v_ego = sm['carState'].vEgo
active = sm['controlsState'].active
measured_curvature = sm['controlsState'].curvature
md = sm['modelV2']
self.LP.parse_model(sm['modelV2'], sm, v_ego)
if len(md.position.x) == TRAJECTORY_SIZE and len(
md.orientation.x) == TRAJECTORY_SIZE:
self.path_xyz = np.column_stack(
[md.position.x, md.position.y, md.position.z])
self.t_idxs = np.array(md.position.t)
self.plan_yaw = list(md.orientation.z)
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (
abs(self.output_scale) >= 0.9 and self.lane_change_timer > 1):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
torque_applied = sm['carState'].steeringPressed and \
((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
blindspot_detected = (
(sm['carState'].leftBlindspot
and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].rightBlindspot
and self.lane_change_direction == LaneChangeDirection.right))
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
self.lane_change_state = LaneChangeState.preLaneChange
self.lane_change_ll_prob = 1.0
self.lane_change_wait_timer = 0
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
self.lane_change_wait_timer += DT_MDL
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
elif not blindspot_detected and (
torque_applied or (self.lane_change_auto_delay
and self.lane_change_wait_timer >
self.lane_change_auto_delay)):
self.lane_change_state = LaneChangeState.laneChangeStarting
# starting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .5s
self.lane_change_adjust_new = interp(
v_ego, self.lane_change_adjust_vel,
self.lane_change_adjust)
self.lane_change_ll_prob = max(
self.lane_change_ll_prob -
self.lane_change_adjust_new * DT_MDL, 0.0)
# 98% certainty
if lane_change_prob < 0.03 and self.lane_change_ll_prob < 0.02:
self.lane_change_state = LaneChangeState.laneChangeFinishing
# finishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
# fade in laneline over 1s
self.lane_change_ll_prob = min(
self.lane_change_ll_prob + DT_MDL, 1.0)
if one_blinker and self.lane_change_ll_prob > 0.98:
self.lane_change_state = LaneChangeState.preLaneChange
elif self.lane_change_ll_prob > 0.98:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [
LaneChangeState.off, LaneChangeState.preLaneChange
]:
self.lane_change_timer = 0.0
else:
self.lane_change_timer += DT_MDL
self.prev_one_blinker = one_blinker
self.desire = DESIRES[self.lane_change_direction][
self.lane_change_state]
# Turn off lanes during lane change
if self.desire == log.LateralPlan.Desire.laneChangeRight or self.desire == log.LateralPlan.Desire.laneChangeLeft:
self.LP.lll_prob *= self.lane_change_ll_prob
self.LP.rll_prob *= self.lane_change_ll_prob
if self.use_lanelines:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.laneless_mode_status = False
elif not self.use_lanelines and self.laneless_mode == 0:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.laneless_mode_status = False
# use laneless, it might mitigate abrubt steering at stopping?
elif not self.use_lanelines and sm[
'radarState'].leadOne.dRel < 25 and sm[
'radarState'].leadOne.vRel < 0 and (
abs(sm['controlsState'].steeringAngleDesiredDeg) -
abs(sm['carState'].steeringAngleDeg)
) > 2.5 and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
self.laneless_mode_status = True
self.laneless_mode_at_stopping = True
self.laneless_mode_at_stopping_timer = 200
elif self.laneless_mode_at_stopping and (
v_ego < 1 or not self.laneless_mode_at_stopping_timer):
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.laneless_mode_status = False
self.laneless_mode_at_stopping = False
elif not self.use_lanelines and self.laneless_mode == 1:
d_path_xyz = self.path_xyz
self.laneless_mode_status = True
elif not self.use_lanelines and self.laneless_mode == 2 and (
self.LP.lll_prob < 0.3 or self.LP.rll_prob < 0.3
) and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
self.laneless_mode_status = True
self.laneless_mode_status_buffer = True
elif not self.use_lanelines and self.laneless_mode == 2 and (
self.LP.lll_prob > 0.5 and self.LP.rll_prob > 0.5
) and self.laneless_mode_status_buffer and not self.laneless_mode_at_stopping and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
elif not self.use_lanelines and self.laneless_mode == 2 and self.laneless_mode_status_buffer == True and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
self.laneless_mode_status = True
else:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
if self.laneless_mode_at_stopping_timer > 0:
self.laneless_mode_at_stopping_timer -= 1
y_pts = np.interp(v_ego * self.t_idxs[:MPC_N + 1],
np.linalg.norm(d_path_xyz, axis=1), d_path_xyz[:, 1])
heading_pts = np.interp(v_ego * self.t_idxs[:MPC_N + 1],
np.linalg.norm(self.path_xyz, axis=1),
self.plan_yaw)
self.y_pts = y_pts
assert len(y_pts) == MPC_N + 1
assert len(heading_pts) == MPC_N + 1
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
float(v_ego), CAR_ROTATION_RADIUS, list(y_pts),
list(heading_pts))
# init state for next
self.cur_state.x = 0.0
self.cur_state.y = 0.0
self.cur_state.psi = 0.0
self.cur_state.curvature = interp(DT_MDL, self.t_idxs[:MPC_N + 1],
self.mpc_solution.curvature)
# TODO this needs more thought, use .2s extra for now to estimate other delays
delay = CP.steerActuatorDelay + .2
current_curvature = self.mpc_solution.curvature[0]
psi = interp(delay, self.t_idxs[:MPC_N + 1], self.mpc_solution.psi)
next_curvature_rate = self.mpc_solution.curvature_rate[0]
# MPC can plan to turn the wheel and turn back before t_delay. This means
# in high delay cases some corrections never even get commanded. So just use
# psi to calculate a simple linearization of desired curvature
curvature_diff_from_psi = psi / (max(v_ego, 1e-1) *
delay) - current_curvature
next_curvature = current_curvature + 2 * curvature_diff_from_psi
self.desired_curvature = next_curvature
self.desired_curvature_rate = next_curvature_rate
max_curvature_rate = interp(v_ego, MAX_CURVATURE_RATE_SPEEDS,
MAX_CURVATURE_RATES)
self.safe_desired_curvature_rate = clip(self.desired_curvature_rate,
-max_curvature_rate,
max_curvature_rate)
self.safe_desired_curvature = clip(
self.desired_curvature,
self.safe_desired_curvature - max_curvature_rate / DT_MDL,
self.safe_desired_curvature + max_curvature_rate / DT_MDL)
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution.curvature)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.cur_state.curvature = measured_curvature
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
def update(self, sm, CP, VM):
v_ego = sm['carState'].vEgo
active = sm['controlsState'].active
steering_wheel_angle_offset_deg = sm['liveParameters'].angleOffset
steering_wheel_angle_deg = sm['carState'].steeringAngle
# Update vehicle model
x = max(sm['liveParameters'].stiffnessFactor, 0.1)
sr = max(sm['liveParameters'].steerRatio, 0.1)
VM.update_params(x, sr)
curvature_factor = VM.curvature_factor(v_ego)
measured_curvature = -curvature_factor * math.radians(
steering_wheel_angle_deg - steering_wheel_angle_offset_deg) / VM.sR
md = sm['modelV2']
self.LP.parse_model(sm['modelV2'])
if len(md.position.x) == TRAJECTORY_SIZE and len(
md.orientation.x) == TRAJECTORY_SIZE:
self.path_xyz = np.column_stack(
[md.position.x, md.position.y, md.position.z])
self.t_idxs = np.array(md.position.t)
self.plan_yaw = list(md.orientation.z)
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (
not self.lane_change_enabled):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
torque_applied = sm['carState'].steeringPressed and \
((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
blindspot_detected = (
(sm['carState'].leftBlindspot
and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].rightBlindspot
and self.lane_change_direction == LaneChangeDirection.right))
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
self.lane_change_state = LaneChangeState.preLaneChange
self.lane_change_ll_prob = 1.0
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
elif torque_applied and not blindspot_detected:
self.lane_change_state = LaneChangeState.laneChangeStarting
# starting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .5s
self.lane_change_ll_prob = max(
self.lane_change_ll_prob - 2 * DT_MDL, 0.0)
# 98% certainty
if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01:
self.lane_change_state = LaneChangeState.laneChangeFinishing
# finishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
# fade in laneline over 1s
self.lane_change_ll_prob = min(
self.lane_change_ll_prob + DT_MDL, 1.0)
if one_blinker and self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.preLaneChange
elif self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [
LaneChangeState.off, LaneChangeState.preLaneChange
]:
self.lane_change_timer = 0.0
else:
self.lane_change_timer += DT_MDL
self.prev_one_blinker = one_blinker
self.desire = DESIRES[self.lane_change_direction][
self.lane_change_state]
# Turn off lanes during lane change
if self.desire == log.LateralPlan.Desire.laneChangeRight or self.desire == log.LateralPlan.Desire.laneChangeLeft:
self.LP.lll_prob *= self.lane_change_ll_prob
self.LP.rll_prob *= self.lane_change_ll_prob
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
y_pts = np.interp(v_ego * self.t_idxs[:MPC_N + 1],
np.linalg.norm(d_path_xyz, axis=1), d_path_xyz[:, 1])
heading_pts = np.interp(v_ego * self.t_idxs[:MPC_N + 1],
np.linalg.norm(self.path_xyz, axis=1),
self.plan_yaw)
self.y_pts = y_pts
assert len(y_pts) == MPC_N + 1
assert len(heading_pts) == MPC_N + 1
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
float(v_ego), CAR_ROTATION_RADIUS, list(y_pts),
list(heading_pts))
# init state for next
self.cur_state.x = 0.0
self.cur_state.y = 0.0
self.cur_state.psi = 0.0
self.cur_state.curvature = interp(DT_MDL, self.t_idxs[:MPC_N + 1],
self.mpc_solution.curvature)
# TODO this needs more thought, use .2s extra for now to estimate other delays
delay = CP.steerActuatorDelay + .2
next_curvature = interp(delay, self.t_idxs[:MPC_N + 1],
self.mpc_solution.curvature)
psi = interp(delay, self.t_idxs[:MPC_N + 1], self.mpc_solution.psi)
next_curvature_rate = self.mpc_solution.curvature_rate[0]
next_curvature_from_psi = psi / (v_ego * delay)
if psi > self.mpc_solution.curvature[0] * delay * v_ego:
next_curvature = max(next_curvature_from_psi, next_curvature)
else:
next_curvature = min(next_curvature_from_psi, next_curvature)
# reset to current steer angle if not active or overriding
if active:
curvature_desired = next_curvature
desired_curvature_rate = next_curvature_rate
else:
curvature_desired = measured_curvature
desired_curvature_rate = 0.0
# negative sign, controls uses different convention
self.desired_steering_wheel_angle_deg = -float(
math.degrees(curvature_desired * VM.sR) /
curvature_factor) + steering_wheel_angle_offset_deg
self.desired_steering_wheel_angle_rate_deg = -float(
math.degrees(desired_curvature_rate * VM.sR) / curvature_factor)
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution.curvature)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.cur_state.curvature = measured_curvature
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
def update(self, pm, CS, lead):
v_ego = CS.vEgo
# Setup current mpc state
self.cur_state[0].x_ego = 0.0
if lead is not None and lead.status:
x_lead = lead.dRel
v_lead = max(0.0, lead.vLead)
a_lead = lead.aLeadK
if (v_lead < 0.1 or -a_lead / 2.0 > v_lead):
v_lead = 0.0
a_lead = 0.0
self.a_lead_tau = lead.aLeadTau
self.new_lead = False
if not self.prev_lead_status or abs(x_lead -
self.prev_lead_x) > 2.5:
self.libmpc.init_with_simulation(self.v_mpc, x_lead, v_lead,
a_lead, self.a_lead_tau)
self.new_lead = True
self.prev_lead_status = True
self.prev_lead_x = x_lead
self.cur_state[0].x_l = x_lead
self.cur_state[0].v_l = v_lead
else:
self.prev_lead_status = False
# Fake a fast lead car, so mpc keeps running
self.cur_state[0].x_l = 50.0
self.cur_state[0].v_l = v_ego + 10.0
a_lead = 0.0
self.a_lead_tau = _LEAD_ACCEL_TAU
# Calculate mpc
t = sec_since_boot()
# scc smoother
cruise_gap = int(clip(CS.cruiseGap, 1., 4.))
TR = interp(float(cruise_gap), [1., 2., 3., 4.], [1.0, 1.3, 1.7, 2.3])
if self.cruise_gap != cruise_gap:
self.cruise_gap = cruise_gap
self.libmpc.init(MPC_COST_LONG.TTC, MPC_COST_LONG.DISTANCE,
MPC_COST_LONG.ACCELERATION, MPC_COST_LONG.JERK)
n_its = self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
self.a_lead_tau, a_lead, TR)
duration = int((sec_since_boot() - t) * 1e9)
if LOG_MPC:
self.send_mpc_solution(pm, n_its, duration)
# Get solution. MPC timestep is 0.2 s, so interpolation to 0.05 s is needed
self.v_mpc = self.mpc_solution[0].v_ego[1]
self.a_mpc = self.mpc_solution[0].a_ego[1]
self.v_mpc_future = self.mpc_solution[0].v_ego[10]
# Reset if NaN or goes through lead car
crashing = any(lead - ego < -50 for (
lead,
ego) in zip(self.mpc_solution[0].x_l, self.mpc_solution[0].x_ego))
nans = any(math.isnan(x) for x in self.mpc_solution[0].v_ego)
backwards = min(self.mpc_solution[0].v_ego) < -0.01
if ((backwards or crashing) and self.prev_lead_status) or nans:
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning(
"Longitudinal mpc %d reset - backwards: %s crashing: %s nan: %s"
% (self.mpc_id, backwards, crashing, nans))
self.libmpc.init(MPC_COST_LONG.TTC, MPC_COST_LONG.DISTANCE,
MPC_COST_LONG.ACCELERATION, MPC_COST_LONG.JERK)
self.cur_state[0].v_ego = v_ego
self.cur_state[0].a_ego = 0.0
self.v_mpc = v_ego
self.a_mpc = CS.aEgo
self.prev_lead_status = False
def thermald_thread(end_event, hw_queue):
pm = messaging.PubMaster(['deviceState'])
sm = messaging.SubMaster(["peripheralState", "gpsLocationExternal", "controlsState", "pandaStates"], poll=["pandaStates"])
fan_speed = 0
count = 0
onroad_conditions: Dict[str, bool] = {
"ignition": False,
}
startup_conditions: Dict[str, bool] = {}
startup_conditions_prev: Dict[str, bool] = {}
off_ts = None
started_ts = None
started_seen = False
thermal_status = ThermalStatus.green
usb_power = True
last_hw_state = HardwareState(
network_type=NetworkType.none,
network_strength=NetworkStrength.unknown,
network_info=None,
nvme_temps=[],
modem_temps=[],
)
current_filter = FirstOrderFilter(0., CURRENT_TAU, DT_TRML)
temp_filter = FirstOrderFilter(0., TEMP_TAU, DT_TRML)
should_start_prev = False
in_car = False
handle_fan = None
is_uno = False
engaged_prev = False
params = Params()
power_monitor = PowerMonitoring()
HARDWARE.initialize_hardware()
thermal_config = HARDWARE.get_thermal_config()
# TODO: use PI controller for UNO
controller = PIController(k_p=0, k_i=2e-3, k_f=1, neg_limit=-80, pos_limit=0, rate=(1 / DT_TRML))
while not end_event.is_set():
sm.update(PANDA_STATES_TIMEOUT)
pandaStates = sm['pandaStates']
peripheralState = sm['peripheralState']
msg = read_thermal(thermal_config)
if sm.updated['pandaStates'] and len(pandaStates) > 0:
# Set ignition based on any panda connected
onroad_conditions["ignition"] = any(ps.ignitionLine or ps.ignitionCan for ps in pandaStates if ps.pandaType != log.PandaState.PandaType.unknown)
pandaState = pandaStates[0]
in_car = pandaState.harnessStatus != log.PandaState.HarnessStatus.notConnected
usb_power = peripheralState.usbPowerMode != log.PeripheralState.UsbPowerMode.client
# Setup fan handler on first connect to panda
if handle_fan is None and peripheralState.pandaType != log.PandaState.PandaType.unknown:
is_uno = peripheralState.pandaType == log.PandaState.PandaType.uno
if TICI:
cloudlog.info("Setting up TICI fan handler")
handle_fan = handle_fan_tici
elif is_uno or PC:
cloudlog.info("Setting up UNO fan handler")
handle_fan = handle_fan_uno
else:
cloudlog.info("Setting up EON fan handler")
setup_eon_fan()
handle_fan = handle_fan_eon
try:
last_hw_state = hw_queue.get_nowait()
except queue.Empty:
pass
msg.deviceState.freeSpacePercent = get_available_percent(default=100.0)
msg.deviceState.memoryUsagePercent = int(round(psutil.virtual_memory().percent))
msg.deviceState.cpuUsagePercent = [int(round(n)) for n in psutil.cpu_percent(percpu=True)]
msg.deviceState.gpuUsagePercent = int(round(HARDWARE.get_gpu_usage_percent()))
msg.deviceState.networkType = last_hw_state.network_type
msg.deviceState.networkStrength = last_hw_state.network_strength
if last_hw_state.network_info is not None:
msg.deviceState.networkInfo = last_hw_state.network_info
msg.deviceState.nvmeTempC = last_hw_state.nvme_temps
msg.deviceState.modemTempC = last_hw_state.modem_temps
msg.deviceState.screenBrightnessPercent = HARDWARE.get_screen_brightness()
msg.deviceState.batteryPercent = HARDWARE.get_battery_capacity()
msg.deviceState.batteryCurrent = HARDWARE.get_battery_current()
msg.deviceState.usbOnline = HARDWARE.get_usb_present()
current_filter.update(msg.deviceState.batteryCurrent / 1e6)
max_comp_temp = temp_filter.update(
max(max(msg.deviceState.cpuTempC), msg.deviceState.memoryTempC, max(msg.deviceState.gpuTempC))
)
if handle_fan is not None:
fan_speed = handle_fan(controller, max_comp_temp, fan_speed, onroad_conditions["ignition"])
msg.deviceState.fanSpeedPercentDesired = fan_speed
is_offroad_for_5_min = (started_ts is None) and ((not started_seen) or (off_ts is None) or (sec_since_boot() - off_ts > 60 * 5))
if is_offroad_for_5_min and max_comp_temp > OFFROAD_DANGER_TEMP:
# If device is offroad we want to cool down before going onroad
# since going onroad increases load and can make temps go over 107
thermal_status = ThermalStatus.danger
else:
current_band = THERMAL_BANDS[thermal_status]
band_idx = list(THERMAL_BANDS.keys()).index(thermal_status)
if current_band.min_temp is not None and max_comp_temp < current_band.min_temp:
thermal_status = list(THERMAL_BANDS.keys())[band_idx - 1]
elif current_band.max_temp is not None and max_comp_temp > current_band.max_temp:
thermal_status = list(THERMAL_BANDS.keys())[band_idx + 1]
# **** starting logic ****
# Ensure date/time are valid
now = datetime.datetime.utcnow()
startup_conditions["time_valid"] = (now.year > 2020) or (now.year == 2020 and now.month >= 10)
set_offroad_alert_if_changed("Offroad_InvalidTime", (not startup_conditions["time_valid"]))
startup_conditions["up_to_date"] = params.get("Offroad_ConnectivityNeeded") is None or params.get_bool("DisableUpdates") or params.get_bool("SnoozeUpdate")
startup_conditions["not_uninstalling"] = not params.get_bool("DoUninstall")
startup_conditions["accepted_terms"] = params.get("HasAcceptedTerms") == terms_version
# with 2% left, we killall, otherwise the phone will take a long time to boot
startup_conditions["free_space"] = msg.deviceState.freeSpacePercent > 2
startup_conditions["completed_training"] = params.get("CompletedTrainingVersion") == training_version or \
params.get_bool("Passive")
startup_conditions["not_driver_view"] = not params.get_bool("IsDriverViewEnabled")
startup_conditions["not_taking_snapshot"] = not params.get_bool("IsTakingSnapshot")
# if any CPU gets above 107 or the battery gets above 63, kill all processes
# controls will warn with CPU above 95 or battery above 60
onroad_conditions["device_temp_good"] = thermal_status < ThermalStatus.danger
set_offroad_alert_if_changed("Offroad_TemperatureTooHigh", (not onroad_conditions["device_temp_good"]))
if TICI:
missing = (not Path("/data/media").is_mount()) and (not os.path.isfile("/persist/comma/living-in-the-moment"))
set_offroad_alert_if_changed("Offroad_StorageMissing", missing)
# Handle offroad/onroad transition
should_start = all(onroad_conditions.values())
if started_ts is None:
should_start = should_start and all(startup_conditions.values())
if should_start != should_start_prev or (count == 0):
params.put_bool("IsOnroad", should_start)
params.put_bool("IsOffroad", not should_start)
params.put_bool("IsEngaged", False)
engaged_prev = False
HARDWARE.set_power_save(not should_start)
if sm.updated['controlsState']:
engaged = sm['controlsState'].enabled
if engaged != engaged_prev:
params.put_bool("IsEngaged", engaged)
engaged_prev = engaged
try:
with open('/dev/kmsg', 'w') as kmsg:
kmsg.write(f"<3>[thermald] engaged: {engaged}\n")
except Exception:
pass
if should_start:
off_ts = None
if started_ts is None:
started_ts = sec_since_boot()
started_seen = True
else:
if onroad_conditions["ignition"] and (startup_conditions != startup_conditions_prev):
cloudlog.event("Startup blocked", startup_conditions=startup_conditions, onroad_conditions=onroad_conditions)
started_ts = None
if off_ts is None:
off_ts = sec_since_boot()
# Offroad power monitoring
power_monitor.calculate(peripheralState, onroad_conditions["ignition"])
msg.deviceState.offroadPowerUsageUwh = power_monitor.get_power_used()
msg.deviceState.carBatteryCapacityUwh = max(0, power_monitor.get_car_battery_capacity())
current_power_draw = HARDWARE.get_current_power_draw() # pylint: disable=assignment-from-none
msg.deviceState.powerDrawW = current_power_draw if current_power_draw is not None else 0
# Check if we need to disable charging (handled by boardd)
msg.deviceState.chargingDisabled = power_monitor.should_disable_charging(onroad_conditions["ignition"], in_car, off_ts)
# Check if we need to shut down
if power_monitor.should_shutdown(peripheralState, onroad_conditions["ignition"], in_car, off_ts, started_seen):
cloudlog.warning(f"shutting device down, offroad since {off_ts}")
params.put_bool("DoShutdown", True)
msg.deviceState.chargingError = current_filter.x > 0. and msg.deviceState.batteryPercent < 90 # if current is positive, then battery is being discharged
msg.deviceState.started = started_ts is not None
msg.deviceState.startedMonoTime = int(1e9*(started_ts or 0))
last_ping = params.get("LastAthenaPingTime")
if last_ping is not None:
msg.deviceState.lastAthenaPingTime = int(last_ping)
msg.deviceState.thermalStatus = thermal_status
pm.send("deviceState", msg)
if EON and not is_uno:
set_offroad_alert_if_changed("Offroad_ChargeDisabled", (not usb_power))
should_start_prev = should_start
startup_conditions_prev = startup_conditions.copy()
# Log to statsd
statlog.gauge("free_space_percent", msg.deviceState.freeSpacePercent)
statlog.gauge("gpu_usage_percent", msg.deviceState.gpuUsagePercent)
statlog.gauge("memory_usage_percent", msg.deviceState.memoryUsagePercent)
for i, usage in enumerate(msg.deviceState.cpuUsagePercent):
statlog.gauge(f"cpu{i}_usage_percent", usage)
for i, temp in enumerate(msg.deviceState.cpuTempC):
statlog.gauge(f"cpu{i}_temperature", temp)
for i, temp in enumerate(msg.deviceState.gpuTempC):
statlog.gauge(f"gpu{i}_temperature", temp)
statlog.gauge("memory_temperature", msg.deviceState.memoryTempC)
statlog.gauge("ambient_temperature", msg.deviceState.ambientTempC)
for i, temp in enumerate(msg.deviceState.pmicTempC):
statlog.gauge(f"pmic{i}_temperature", temp)
for i, temp in enumerate(last_hw_state.nvme_temps):
statlog.gauge(f"nvme_temperature{i}", temp)
for i, temp in enumerate(last_hw_state.modem_temps):
statlog.gauge(f"modem_temperature{i}", temp)
statlog.gauge("fan_speed_percent_desired", msg.deviceState.fanSpeedPercentDesired)
statlog.gauge("screen_brightness_percent", msg.deviceState.screenBrightnessPercent)
# report to server once every 10 minutes
if (count % int(600. / DT_TRML)) == 0:
if EON and started_ts is None and msg.deviceState.memoryUsagePercent > 40:
cloudlog.event("High offroad memory usage", mem=msg.deviceState.memoryUsagePercent)
cloudlog.event("STATUS_PACKET",
count=count,
pandaStates=[strip_deprecated_keys(p.to_dict()) for p in pandaStates],
peripheralState=strip_deprecated_keys(peripheralState.to_dict()),
location=(strip_deprecated_keys(sm["gpsLocationExternal"].to_dict()) if sm.alive["gpsLocationExternal"] else None),
deviceState=strip_deprecated_keys(msg.to_dict()))
count += 1
def update(self, sm, pm, CP, VM):
v_ego = sm['carState'].vEgo
angle_steers = sm['carState'].steeringAngle
active = sm['controlsState'].active
angle_offset = sm['liveParameters'].angleOffset
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
# Update vehicle model
x = max(sm['liveParameters'].stiffnessFactor, 0.1)
sr = max(sm['liveParameters'].steerRatio, 0.1)
VM.update_params(x, sr)
curvature_factor = VM.curvature_factor(v_ego)
self.LP.parse_model(sm['model'])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX) or (
not one_blinker) or (not self.lane_change_enabled):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
torque_applied = sm['carState'].steeringPressed and \
((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
self.lane_change_state = LaneChangeState.preLaneChange
self.lane_change_ll_prob = 1.0
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
elif torque_applied:
self.lane_change_state = LaneChangeState.laneChangeStarting
# starting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .5s
self.lane_change_ll_prob = max(
self.lane_change_ll_prob - 2 * DT_MDL, 0.0)
# 98% certainty
if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01:
self.lane_change_state = LaneChangeState.laneChangeFinishing
# finishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
# fade in laneline over 1s
self.lane_change_ll_prob = min(
self.lane_change_ll_prob + DT_MDL, 1.0)
if one_blinker and self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.preLaneChange
elif self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [
LaneChangeState.off, LaneChangeState.preLaneChange
]:
self.lane_change_timer = 0.0
else:
self.lane_change_timer += DT_MDL
self.prev_one_blinker = one_blinker
desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Turn off lanes during lane change
if desire == log.PathPlan.Desire.laneChangeRight or desire == log.PathPlan.Desire.laneChangeLeft:
self.LP.l_prob *= self.lane_change_ll_prob
self.LP.r_prob *= self.lane_change_ll_prob
self.LP.update_d_poly(v_ego)
# account for actuation delay
self.cur_state = calc_states_after_delay(self.cur_state, v_ego,
angle_steers - angle_offset,
curvature_factor, VM.sR,
CP.steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly),
list(self.LP.d_poly), self.LP.l_prob,
self.LP.r_prob, curvature_factor, v_ego_mpc,
self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
rate_desired = math.degrees(self.mpc_solution[0].rate[0] * VM.sR)
else:
delta_desired = math.radians(angle_steers - angle_offset) / VM.sR
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * VM.sR) + angle_offset)
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, CP.steerRateCost)
self.cur_state[0].delta = math.radians(angle_steers -
angle_offset) / VM.sR
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=[
'carState', 'controlsState', 'liveParameters', 'model'
])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
plan_send.pathPlan.angleOffset = float(
sm['liveParameters'].angleOffsetAverage)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.desire = desire
plan_send.pathPlan.laneChangeState = self.lane_change_state
plan_send.pathPlan.laneChangeDirection = self.lane_change_direction
pm.send('pathPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
def update(self, CS, lead, v_cruise_setpoint):
# Setup current mpc state
self.cur_state[0].x_ego = 0.0
if lead is not None and lead.status:
x_lead = lead.dRel
v_lead = max(0.0, lead.vLead)
a_lead = lead.aLeadK
if (v_lead < 0.1 or -a_lead / 2.0 > v_lead):
v_lead = 0.0
a_lead = 0.0
self.a_lead_tau = max(lead.aLeadTau, (a_lead**2 * math.pi) / (2 * (v_lead + 0.01)**2))
self.new_lead = False
if not self.prev_lead_status or abs(x_lead - self.prev_lead_x) > 2.5:
self.libmpc.init_with_simulation(self.v_mpc, x_lead, v_lead, a_lead, self.a_lead_tau)
self.new_lead = True
self.prev_lead_status = True
self.prev_lead_x = x_lead
self.cur_state[0].x_l = x_lead
self.cur_state[0].v_l = v_lead
else:
self.prev_lead_status = False
# Fake a fast lead car, so mpc keeps running
self.cur_state[0].x_l = 50.0
self.cur_state[0].v_l = CS.vEgo + 10.0
a_lead = 0.0
self.a_lead_tau = _LEAD_ACCEL_TAU
# Calculate mpc
t = sec_since_boot()
n_its = self.libmpc.run_mpc(self.cur_state, self.mpc_solution, self.a_lead_tau, a_lead)
duration = int((sec_since_boot() - t) * 1e9)
self.send_mpc_solution(n_its, duration)
# Get solution. MPC timestep is 0.2 s, so interpolation to 0.05 s is needed
self.v_mpc = self.mpc_solution[0].v_ego[1]
self.a_mpc = self.mpc_solution[0].a_ego[1]
self.v_mpc_future = self.mpc_solution[0].v_ego[10]
# Reset if NaN or goes through lead car
dls = np.array(list(self.mpc_solution[0].x_l)) - np.array(list(self.mpc_solution[0].x_ego))
crashing = min(dls) < -50.0
nans = np.any(np.isnan(list(self.mpc_solution[0].v_ego)))
backwards = min(list(self.mpc_solution[0].v_ego)) < -0.01
if ((backwards or crashing) and self.prev_lead_status) or nans:
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Longitudinal mpc %d reset - backwards: %s crashing: %s nan: %s" % (
self.mpc_id, backwards, crashing, nans))
self.libmpc.init(MPC_COST_LONG.TTC, MPC_COST_LONG.DISTANCE,
MPC_COST_LONG.ACCELERATION, MPC_COST_LONG.JERK)
self.cur_state[0].v_ego = CS.vEgo
self.cur_state[0].a_ego = 0.0
self.v_mpc = CS.vEgo
self.a_mpc = CS.aEgo
self.prev_lead_status = False
