class PathPlanner():
def __init__(self, CP):
self.LP = LanePlanner()
self.last_cloudlog_t = 0
self.setup_mpc(CP.steerRateCost)
self.solution_invalid_cnt = 0
self.path_offset_i = 0.0
self.mpc_frame = 0
self.sR_delay_counter = 0
self.steerRatio_new = 0.0
self.sR_time = 1
kegman = kegman_conf(CP)
if kegman.conf['steerRatio'] == "-1":
self.steerRatio = CP.steerRatio
else:
self.steerRatio = float(kegman.conf['steerRatio'])
if kegman.conf['steerRateCost'] == "-1":
self.steerRateCost = CP.steerRateCost
else:
self.steerRateCost = float(kegman.conf['steerRateCost'])
self.sR = [
float(kegman.conf['steerRatio']),
(float(kegman.conf['steerRatio']) + float(kegman.conf['sR_boost']))
]
self.sRBP = [
float(kegman.conf['sR_BP0']),
float(kegman.conf['sR_BP1'])
]
self.steerRateCost_prev = self.steerRateCost
self.setup_mpc(self.steerRateCost)
def setup_mpc(self, steer_rate_cost):
self.libmpc = libmpc_py.libmpc
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, steer_rate_cost)
self.mpc_solution = libmpc_py.ffi.new("log_t *")
self.cur_state = libmpc_py.ffi.new("state_t *")
self.cur_state[0].x = 0.0
self.cur_state[0].y = 0.0
self.cur_state[0].psi = 0.0
self.cur_state[0].delta = 0.0
self.angle_steers_des = 0.0
self.angle_steers_des_mpc = 0.0
self.angle_steers_des_prev = 0.0
self.angle_steers_des_time = 0.0
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)
# 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
kegman = kegman_conf()
if kegman.conf['tuneGernby'] == "1":
self.steerRateCost = float(kegman.conf['steerRateCost'])
if self.steerRateCost != self.steerRateCost_prev:
self.setup_mpc(self.steerRateCost)
self.steerRateCost_prev = self.steerRateCost
self.sR = [
float(kegman.conf['steerRatio']),
(float(kegman.conf['steerRatio']) +
float(kegman.conf['sR_boost']))
]
self.sRBP = [
float(kegman.conf['sR_BP0']),
float(kegman.conf['sR_BP1'])
]
self.sR_time = int(float(kegman.conf['sR_time'])) * 100
self.mpc_frame = 0
if v_ego > 11.111:
# 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)
# 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,
self.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,
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, self.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()
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)
class PathPlanner():
def __init__(self, CP):
self.LP = LanePlanner(shouldUseAlca=True)
self.last_cloudlog_t = 0
self.setup_mpc(CP.steerRateCost)
self.solution_invalid_cnt = 0
self.path_offset_i = 0.0
def setup_mpc(self, steer_rate_cost):
self.libmpc = libmpc_py.libmpc
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, steer_rate_cost)
self.mpc_solution = libmpc_py.ffi.new("log_t *")
self.cur_state = libmpc_py.ffi.new("state_t *")
self.cur_state[0].x = 0.0
self.cur_state[0].y = 0.0
self.cur_state[0].psi = 0.0
self.cur_state[0].delta = 0.0
self.angle_steers_des = 0.0
self.angle_steers_des_mpc = 0.0
self.angle_steers_des_prev = 0.0
self.angle_steers_des_time = 0.0
def update(self, sm, pm, CP, VM):
v_ego = sm['carState'].vEgo
angle_steers = sm['carState'].steeringAngle
#angle_steers_des = sm['controlsState'].angleSteersDes
active = sm['controlsState'].active
angle_offset = sm['liveParameters'].angleOffset
self.LP.update(v_ego, sm['model'], True)
# 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 = 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)
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)
