class LateralPlanner():
def __init__(self, CP):
self.LP = LanePlanner()
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.setup_mpc()
self.solution_invalid_cnt = 0
self.use_lanelines = Params().get('EndToEndToggle') != b'1'
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
self.desire = log.LateralPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE, ))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
self.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].curvature = 0.0
self.desired_curvature = 0.0
self.safe_desired_curvature = 0.0
self.desired_curvature_rate = 0.0
self.safe_desired_curvature_rate = 0.0
def update(self, sm, CP):
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)
# 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):
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
if self.use_lanelines:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
else:
d_path_xyz = 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
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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_alive_and_valid(
service_list=['carState', 'controlsState', 'modelV2'])
plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
plan_send.lateralPlan.dProb = float(self.LP.d_prob)
plan_send.lateralPlan.rawCurvature = float(self.desired_curvature)
plan_send.lateralPlan.rawCurvatureRate = float(
self.desired_curvature_rate)
plan_send.lateralPlan.curvature = float(self.safe_desired_curvature)
plan_send.lateralPlan.curvatureRate = float(
self.safe_desired_curvature_rate)
plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.lateralPlan.desire = self.desire
plan_send.lateralPlan.laneChangeState = self.lane_change_state
plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
pm.send('lateralPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution.x)
dat.liveMpc.y = list(self.mpc_solution.y)
dat.liveMpc.psi = list(self.mpc_solution.psi)
dat.liveMpc.curvature = list(self.mpc_solution.curvature)
dat.liveMpc.cost = self.mpc_solution.cost
pm.send('liveMpc', dat)
class LateralPlanner():
def __init__(self, CP):
self.LP = LanePlanner()
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.setup_mpc()
self.solution_invalid_cnt = 0
self.lane_change_enabled = Params().get('LaneChangeEnabled') == b'1'
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
self.desire = log.LateralPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE,3))
self.path_xyz_stds = np.ones((TRAJECTORY_SIZE,3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE,))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
self.kegman = kegman_conf(CP)
self.mpc_frame = 0
self.model_laneless = "0"
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, self.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].curvature = 0.0
self.angle_steers_des = 0.0
self.angle_steers_des_mpc = 0.0
self.angle_steers_des_time = 0.0
def update(self, sm, CP, VM):
v_ego = sm['carState'].vEgo
active = sm['controlsState'].active
steering_wheel_angle_offset_deg = sm['liveParameters'].angleOffsetDeg
steering_wheel_angle_deg = sm['carState'].steeringAngleDeg
# 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)
if len(md.orientation.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 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
self.mpc_frame += 1
if self.mpc_frame % 1000 == 0:
# live tuning through /data/openpilot/tune.py for laneless toggle
self.kegman = kegman_conf()
self.model_laneless = float(self.kegman.conf['laneLess'])
self.mpc_frame = 0
if self.model_laneless == 0:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.init_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, CP.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, 5.0) * 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.libmpc.init_weights(path_cost, heading_cost, CP.steerRateCost)
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
# 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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'liveParameters', 'modelV2'])
plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
plan_send.lateralPlan.dProb = float(self.LP.d_prob)
plan_send.lateralPlan.steeringAngleDeg = float(self.desired_steering_wheel_angle_deg)
plan_send.lateralPlan.steeringRateDeg = float(self.desired_steering_wheel_angle_rate_deg)
plan_send.lateralPlan.angleOffsetDeg = float(sm['liveParameters'].angleOffsetAverageDeg)
plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.lateralPlan.desire = self.desire
plan_send.lateralPlan.laneChangeState = self.lane_change_state
plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
pm.send('lateralPlan', 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.tire_angle = list(self.mpc_solution[0].tire_angle)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
class PathPlanner():
def __init__(self, CP):
self.LP = LanePlanner()
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.setup_mpc()
self.solution_invalid_cnt = 0
self.lane_change_enabled = Params().get('LaneChangeEnabled') == b'1'
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
self.desire = log.PathPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE, ))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
self.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].tire_angle = 0.0
self.angle_steers_des = 0.0
self.angle_steers_des_mpc = 0.0
self.angle_steers_des_time = 0.0
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
measured_tire_angle = -math.radians(
steering_wheel_angle_deg - steering_wheel_angle_offset_deg) / VM.sR
# 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)
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.PathPlan.Desire.laneChangeRight or self.desire == log.PathPlan.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
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
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_mpc),
curvature_factor, 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.tire_angle = interp(DT_MDL, self.t_idxs[:MPC_N + 1],
self.mpc_solution.tire_angle)
# TODO this needs more thought, use .2s extra for now to estimate other delays
delay = CP.steerActuatorDelay + .2
next_tire_angle = interp(DT_MDL + delay, self.t_idxs[:MPC_N + 1],
self.mpc_solution.tire_angle)
next_tire_angle_rate = self.mpc_solution.tire_angle_rate[0]
# reset to current steer angle if not active or overriding
if active:
tire_angle_desired = next_tire_angle
desired_tire_angle_rate = next_tire_angle_rate
else:
tire_angle_desired = measured_tire_angle
desired_tire_angle_rate = 0.0
# negative sign, controls uses different convention
self.desired_steering_wheel_angle_deg = -float(
math.degrees(
tire_angle_desired * VM.sR)) + steering_wheel_angle_offset_deg
self.desired_steering_wheel_angle_rate_deg = -float(
math.degrees(desired_tire_angle_rate * VM.sR))
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution.tire_angle)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.cur_state.tire_angle = measured_tire_angle
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 publish(self, sm, pm):
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', 'modelV2'
])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPathPoints = [float(x) for x in self.y_pts]
plan_send.pathPlan.lProb = float(self.LP.lll_prob)
plan_send.pathPlan.rProb = float(self.LP.rll_prob)
plan_send.pathPlan.dProb = float(self.LP.d_prob)
plan_send.pathPlan.angleSteers = float(
self.desired_steering_wheel_angle_deg)
plan_send.pathPlan.rateSteers = float(
self.desired_steering_wheel_angle_rate_deg)
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 = self.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.tire_angle = list(self.mpc_solution[0].tire_angle)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
class LateralPlanner():
def __init__(self, CP, use_lanelines=True, wide_camera=False):
self.use_lanelines = use_lanelines
self.LP = LanePlanner(wide_camera)
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.solution_invalid_cnt = 0
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.keep_pulse_timer = 0.0
self.prev_one_blinker = False
self.desire = log.LateralPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.path_xyz_stds = np.ones((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE, ))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
self.lat_mpc = LateralMpc()
self.reset_mpc(np.zeros(6))
def reset_mpc(self, x0=np.zeros(6)):
self.x0 = x0
self.lat_mpc.reset(x0=self.x0)
self.desired_curvature = 0.0
self.safe_desired_curvature = 0.0
self.desired_curvature_rate = 0.0
self.safe_desired_curvature_rate = 0.0
def update(self, sm, CP):
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):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
# LaneChangeState.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
# LaneChangeState.preLaneChange
elif self.lane_change_state == LaneChangeState.preLaneChange:
# Set lane change direction
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
else: # If there are no blinkers we will go back to LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
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))
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
# 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
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
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 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]
# 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,
CP.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,
5.0) * 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, CP.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
self.lat_mpc.run(self.x0, v_ego, CAR_ROTATION_RADIUS, 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 infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.lat_mpc.x_sol[:, 3])
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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_alive_and_valid(
service_list=['carState', 'controlsState', 'modelV2'])
plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
plan_send.lateralPlan.psis = [
float(x) for x in self.lat_mpc.x_sol[0:CONTROL_N, 2]
]
plan_send.lateralPlan.curvatures = [
float(x) for x in self.lat_mpc.x_sol[0:CONTROL_N, 3]
]
plan_send.lateralPlan.curvatureRates = [
float(x) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]
] + [0.0]
plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
plan_send.lateralPlan.dProb = float(self.LP.d_prob)
plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.lateralPlan.desire = self.desire
plan_send.lateralPlan.useLaneLines = self.use_lanelines
plan_send.lateralPlan.laneChangeState = self.lane_change_state
plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
pm.send('lateralPlan', plan_send)
class LateralPlanner():
def __init__(self, CP, use_lanelines=True, wide_camera=False):
self.use_lanelines = use_lanelines
self.LP = LanePlanner(wide_camera)
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.setup_mpc()
self.solution_invalid_cnt = 0
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
self.desire = log.LateralPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.path_xyz_stds = np.ones((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE, ))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
# dp
self.dp_lc_auto_allowed = False
self.dp_lc_auto_timer = None
self.dp_lc_auto_delay = 2.
self.dp_lc_auto_cont = False
self.dp_lc_auto_completed = False
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init()
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].curvature = 0.0
self.desired_curvature = 0.0
self.safe_desired_curvature = 0.0
self.desired_curvature_rate = 0.0
self.safe_desired_curvature_rate = 0.0
def update(self, sm, CP):
v_ego = sm['carState'].vEgo
active = sm['controlsState'].active
measured_curvature = sm['controlsState'].curvature
self.LP.update_dp_set_offsets(sm['dragonConf'].dpCameraOffset,
sm['dragonConf'].dpPathOffset)
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.orientation.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 < (sm['dragonConf'].dpLcMinMph *
CV.MPH_TO_MS)
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
# LaneChangeState.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
# dp alc
cur_time = sec_since_boot()
if not below_lane_change_speed and sm[
'dragonConf'].dpLateralMode == 2 and v_ego >= (
sm['dragonConf'].dpLcAutoMinMph * CV.MPH_TO_MS):
# we allow auto lc when speed reached dragon_auto_lc_min_mph
self.dp_lc_auto_allowed = True
else:
# if too slow, we reset all the variables
self.dp_lc_auto_allowed = False
self.dp_lc_auto_timer = None
# disable auto lc when continuous is off and already did auto lc once
if self.dp_lc_auto_allowed and not sm[
'dragonConf'].dpLcAutoCont and self.dp_lc_auto_completed:
self.dp_lc_auto_allowed = False
# LaneChangeState.preLaneChange
elif self.lane_change_state == LaneChangeState.preLaneChange:
# Set lane change direction
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
else: # If there are no blinkers we will go back to LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
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))
# dp alc
if self.dp_lc_auto_allowed:
if self.dp_lc_auto_timer is None:
self.dp_lc_auto_timer = cur_time + sm[
'dragonConf'].dpLcAutoDelay
elif cur_time >= self.dp_lc_auto_timer:
# if timer is up, we set torque_applied to True to fake user input
torque_applied = True
self.dp_lc_auto_completed = True
# we reset the timers when torque is applied regardless
if torque_applied and not blindspot_detected:
self.dp_lc_auto_timer = None
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
# 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
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
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 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
# dp when finishing, we reset timer to none.
self.dp_lc_auto_timer = None
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.prev_one_blinker and not one_blinker:
self.dp_lc_auto_completed = False
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.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.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,
5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.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
# for now CAR_ROTATION_RADIUS is disabled
# to use it, enable it in the MPC
assert abs(CAR_ROTATION_RADIUS) < 1e-3
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[:LAT_MPC_N + 1],
self.mpc_solution.curvature)
# 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()
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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=[
'carState', 'controlsState', 'modelV2', 'dragonConf'
])
plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
plan_send.lateralPlan.psis = [
float(x) for x in self.mpc_solution.psi[0:CONTROL_N]
]
plan_send.lateralPlan.curvatures = [
float(x) for x in self.mpc_solution.curvature[0:CONTROL_N]
]
plan_send.lateralPlan.curvatureRates = [
float(x) for x in self.mpc_solution.curvature_rate[0:CONTROL_N - 1]
] + [0.0]
plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
plan_send.lateralPlan.dProb = float(self.LP.d_prob)
plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.lateralPlan.desire = self.desire
plan_send.lateralPlan.laneChangeState = self.lane_change_state
plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
plan_send.lateralPlan.dpALCAllowed = self.dp_lc_auto_allowed
pm.send('lateralPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution.x)
dat.liveMpc.y = list(self.mpc_solution.y)
dat.liveMpc.psi = list(self.mpc_solution.psi)
dat.liveMpc.curvature = list(self.mpc_solution.curvature)
dat.liveMpc.cost = self.mpc_solution.cost
pm.send('liveMpc', dat)
class LateralPlanner():
def __init__(self, CP, use_lanelines=True, wide_camera=False):
self.use_lanelines = use_lanelines
self.LP = LanePlanner(wide_camera)
self.last_cloudlog_t = 0
self.setup_mpc()
self.solution_invalid_cnt = 0
self.laneless_mode = int(Params().get("LanelessMode", encoding="utf8"))
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
self.nudgeless_enabled = Params().get_bool("NudgelessLaneChange")
self.nudgeless_delay = 3.0 # [s] amount of time blinker has to be on before nudgless lane change
self.lane_change_state = LaneChangeState.off
self.prev_lane_change_state = self.lane_change_state
self.preLaneChange_start_t = 0.
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.keep_pulse_timer = 0.0
self.prev_one_blinker = False
self.desire = log.LateralPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE,3))
self.path_xyz_stds = np.ones((TRAJECTORY_SIZE,3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE,))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
self.d_path_w_lines_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.second = 0.0
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init()
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].curvature = 0.0
self.desired_curvature = 0.0
self.safe_desired_curvature = 0.0
self.desired_curvature_rate = 0.0
self.safe_desired_curvature_rate = 0.0
def update(self, sm, CP):
self.second += DT_MDL
if self.second > 1.0:
self.use_lanelines = not Params().get_bool("EndToEndToggle")
self.laneless_mode = int(Params().get("LanelessMode", encoding="utf8"))
self.second = 0.0
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.orientation.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):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
# LaneChangeState.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
# LaneChangeState.preLaneChange
elif self.lane_change_state == LaneChangeState.preLaneChange:
t = sec_since_boot()
if self.prev_lane_change_state in [LaneChangeState.off, LaneChangeState.laneChangeFinishing] and t - self.preLaneChange_start_t > 3.:
self.preLaneChange_start_t = t
# Set lane change direction
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
else: # If there are no blinkers we will go back to LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
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))
torque_applied = torque_applied or (self.nudgeless_enabled and t - self.preLaneChange_start_t > self.nudgeless_delay)
blindspot_detected = ((sm['carState'].leftBlindspot and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].rightBlindspot and self.lane_change_direction == LaneChangeDirection.right))
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
# 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
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
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 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
self.prev_lane_change_state = self.lane_change_state
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]
# 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
self.d_path_w_lines_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
if self.use_lanelines:
d_path_xyz = self.d_path_w_lines_xyz
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, CP.steerRateCost)
self.laneless_mode_status = False
elif self.laneless_mode == 0:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, CP.steerRateCost)
self.laneless_mode_status = False
elif self.laneless_mode == 1:
d_path_xyz = self.path_xyz
path_cost = np.clip(abs(self.path_xyz[0,1]/self.path_xyz_stds[0,1]), 0.5, 5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
elif self.laneless_mode == 2 and ((self.LP.lll_prob + self.LP.rll_prob)/2 < 0.3) and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
path_cost = np.clip(abs(self.path_xyz[0,1]/self.path_xyz_stds[0,1]), 0.5, 5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
self.laneless_mode_status_buffer = True
elif self.laneless_mode == 2 and ((self.LP.lll_prob + self.LP.rll_prob)/2 > 0.5) and \
self.laneless_mode_status_buffer and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, CP.steerRateCost)
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
elif self.laneless_mode == 2 and self.laneless_mode_status_buffer == True and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
path_cost = np.clip(abs(self.path_xyz[0,1]/self.path_xyz_stds[0,1]), 0.5, 5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
else:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING, CP.steerRateCost)
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
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
# for now CAR_ROTATION_RADIUS is disabled
# to use it, enable it in the MPC
assert abs(CAR_ROTATION_RADIUS) < 1e-3
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[:LAT_MPC_N + 1], self.mpc_solution.curvature)
# 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()
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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'modelV2'])
plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
plan_send.lateralPlan.psis = [float(x) for x in self.mpc_solution.psi[0:CONTROL_N]]
plan_send.lateralPlan.curvatures = [float(x) for x in self.mpc_solution.curvature[0:CONTROL_N]]
plan_send.lateralPlan.curvatureRates = [float(x) for x in self.mpc_solution.curvature_rate[0:CONTROL_N-1]] +[0.0]
plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
plan_send.lateralPlan.dProb = float(self.LP.d_prob)
plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.lateralPlan.desire = self.desire
plan_send.lateralPlan.laneChangeState = self.lane_change_state
plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
plan_send.lateralPlan.dPathWLinesX = [float(x) for x in self.d_path_w_lines_xyz[:, 0]]
plan_send.lateralPlan.dPathWLinesY = [float(y) for y in self.d_path_w_lines_xyz[:, 1]]
plan_send.lateralPlan.lanelessMode = bool(self.laneless_mode_status)
pm.send('lateralPlan', plan_send)
class LateralPlanner():
def __init__(self, CP, use_lanelines=True, wide_camera=False):
self.use_lanelines = use_lanelines
self.LP = LanePlanner(wide_camera)
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.setup_mpc()
self.solution_invalid_cnt = 0
self.use_lanelines = False
self.laneless_mode = 0
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
self.laneless_mode_at_stopping = False
self.laneless_mode_at_stopping_timer = 0
if int(Params().get("OpkrAutoLaneChangeDelay", encoding="utf8")) == 0:
self.lane_change_auto_delay = 0.0
elif int(Params().get("OpkrAutoLaneChangeDelay",
encoding="utf8")) == 1:
self.lane_change_auto_delay = 0.2
elif int(Params().get("OpkrAutoLaneChangeDelay",
encoding="utf8")) == 2:
self.lane_change_auto_delay = 0.5
elif int(Params().get("OpkrAutoLaneChangeDelay",
encoding="utf8")) == 3:
self.lane_change_auto_delay = 1.0
elif int(Params().get("OpkrAutoLaneChangeDelay",
encoding="utf8")) == 4:
self.lane_change_auto_delay = 1.5
elif int(Params().get("OpkrAutoLaneChangeDelay",
encoding="utf8")) == 5:
self.lane_change_auto_delay = 2.0
self.lane_change_wait_timer = 0.0
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
self.desire = log.LateralPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.path_xyz_stds = np.ones((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE, ))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
self.lane_change_adjust = [0.11, 0.18, 0.75, 1.25]
self.lane_change_adjust_vel = [8.3, 16, 22, 30]
self.lane_change_adjust_new = 0.0
self.standstill_elapsed_time = 0.0
self.v_cruise_kph = 0
self.stand_still = False
self.steer_actuator_delay_to_send = 0.0
self.output_scale = 0.0
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init()
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].curvature = 0.0
self.desired_curvature = 0.0
self.safe_desired_curvature = 0.0
self.desired_curvature_rate = 0.0
self.safe_desired_curvature_rate = 0.0
def update(self, sm, CP):
self.use_lanelines = not Params().get_bool("EndToEndToggle")
self.laneless_mode = int(Params().get(
"LanelessMode", encoding="utf8")) if Params().get(
"LanelessMode", encoding="utf8") is not None else 0
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)
if len(md.orientation.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 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) >=
(CP.steerMaxV[0] - 0.15) 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.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.laneless_mode_status = False
elif self.laneless_mode == 0:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.laneless_mode_status = False
# use laneless, it might mitigate abrubt steering at stopping?
elif sm['radarState'].leadOne.dRel < 25 and (
sm['radarState'].leadOne.vRel < 0 or
(sm['radarState'].leadOne.vRel >= 0 and v_ego < 5)
) and (abs(sm['controlsState'].steeringAngleDesiredDeg) -
abs(sm['carState'].steeringAngleDeg)
) > 2 and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
self.laneless_mode_at_stopping = True
self.laneless_mode_at_stopping_timer = 80
elif self.laneless_mode_at_stopping and (
v_ego < 0.5 or self.laneless_mode_at_stopping_timer <= 0):
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.laneless_mode_status = False
self.laneless_mode_at_stopping = False
elif self.laneless_mode == 1:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
elif self.laneless_mode == 2 and (
(self.LP.lll_prob + self.LP.rll_prob) / 2 <
0.3) and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
self.laneless_mode_status_buffer = True
elif self.laneless_mode == 2 and (
(self.LP.lll_prob + self.LP.rll_prob) / 2 > 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.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
elif self.laneless_mode == 2 and self.laneless_mode_status_buffer == True and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
else:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
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
#delay = CP.steerActuatorDelay + .2
self.steer_actuator_delay_to_send = delay
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()
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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 3
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_alive_and_valid(
service_list=['carState', 'controlsState', 'modelV2'])
plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
plan_send.lateralPlan.dProb = float(self.LP.d_prob)
plan_send.lateralPlan.rawCurvature = float(self.desired_curvature)
plan_send.lateralPlan.rawCurvatureRate = float(
self.desired_curvature_rate)
plan_send.lateralPlan.curvature = float(self.safe_desired_curvature)
plan_send.lateralPlan.curvatureRate = float(
self.safe_desired_curvature_rate)
plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.lateralPlan.desire = self.desire
plan_send.lateralPlan.laneChangeState = self.lane_change_state
plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
plan_send.lateralPlan.steerRateCost = float(self.steer_rate_cost)
plan_send.lateralPlan.outputScale = float(self.output_scale)
plan_send.lateralPlan.vCruiseSet = float(self.v_cruise_kph)
plan_send.lateralPlan.vCurvature = float(sm['controlsState'].curvature)
plan_send.lateralPlan.steerAngleDesireDeg = float(
sm['controlsState'].steeringAngleDesiredDeg)
plan_send.lateralPlan.lanelessMode = bool(self.laneless_mode_status)
plan_send.lateralPlan.steerActuatorDelay = float(
self.steer_actuator_delay_to_send)
if self.stand_still:
self.standstill_elapsed_time += DT_MDL
else:
self.standstill_elapsed_time = 0.0
plan_send.lateralPlan.standstillElapsedTime = int(
self.standstill_elapsed_time)
pm.send('lateralPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution.x)
dat.liveMpc.y = list(self.mpc_solution.y)
dat.liveMpc.psi = list(self.mpc_solution.psi)
dat.liveMpc.curvature = list(self.mpc_solution.curvature)
dat.liveMpc.cost = self.mpc_solution.cost
pm.send('liveMpc', dat)
class LateralPlanner:
def __init__(self, use_lanelines=True, wide_camera=False):
self.use_lanelines = use_lanelines
self.LP = LanePlanner(wide_camera)
self.DH = DesireHelper()
self.last_cloudlog_t = 0
self.solution_invalid_cnt = 0
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.path_xyz_stds = np.ones((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE, ))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
self.lat_mpc = LateralMpc()
self.reset_mpc(np.zeros(4))
def reset_mpc(self, x0=np.zeros(4)):
self.x0 = x0
self.lat_mpc.reset(x0=self.x0)
def update(self, sm):
v_ego = sm['carState'].vEgo
measured_curvature = sm['controlsState'].curvature
# Parse model predictions
md = sm['modelV2']
self.LP.parse_model(md)
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
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
self.DH.update(sm['carState'], sm['controlsState'].active,
lane_change_prob)
# Turn off lanes during lane change
if self.DH.desire == log.LateralPlan.Desire.laneChangeRight or self.DH.desire == log.LateralPlan.Desire.laneChangeLeft:
self.LP.lll_prob *= self.DH.lane_change_ll_prob
self.LP.rll_prob *= self.DH.lane_change_ll_prob
# Calculate final driving path and set MPC costs
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,
MPC_COST_LAT.STEER_RATE)
else:
d_path_xyz = self.path_xyz
# 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, 0.15])
self.lat_mpc.set_weights(MPC_COST_LAT.PATH, heading_cost,
MPC_COST_LAT.STEER_RATE)
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
# mpc.u_sol is the desired curvature rate given x0 curv state.
# with x0[3] = measured_curvature, this would be the actual desired rate.
# instead, interpolate x_sol so that x0[3] is the desired curvature for lat_control.
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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_checks(
service_list=['carState', 'controlsState', 'modelV2'])
lateralPlan = plan_send.lateralPlan
lateralPlan.modelMonoTime = sm.logMonoTime['modelV2']
lateralPlan.laneWidth = float(self.LP.lane_width)
lateralPlan.dPathPoints = self.y_pts.tolist()
lateralPlan.psis = self.lat_mpc.x_sol[0:CONTROL_N, 2].tolist()
lateralPlan.curvatures = self.lat_mpc.x_sol[0:CONTROL_N, 3].tolist()
lateralPlan.curvatureRates = [
float(x) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]
] + [0.0]
lateralPlan.lProb = float(self.LP.lll_prob)
lateralPlan.rProb = float(self.LP.rll_prob)
lateralPlan.dProb = float(self.LP.d_prob)
lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
lateralPlan.solverExecutionTime = self.lat_mpc.solve_time
lateralPlan.desire = self.DH.desire
lateralPlan.useLaneLines = self.use_lanelines
lateralPlan.laneChangeState = self.DH.lane_change_state
lateralPlan.laneChangeDirection = self.DH.lane_change_direction
pm.send('lateralPlan', plan_send)
class LateralPlanner():
def __init__(self, CP, use_lanelines=True, wide_camera=False):
self.use_lanelines = use_lanelines
self.LP = LanePlanner(wide_camera)
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.solution_invalid_cnt = 0
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.keep_pulse_timer = 0.0
self.prev_one_blinker = False
self.desire = log.LateralPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.path_xyz_stds = np.ones((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE, ))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
self.d_path_w_lines_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.lat_mpc = LateralMpc()
self.reset_mpc(np.zeros(6))
self.d_path_w_lines_xyz = np.zeros((TRAJECTORY_SIZE, 3))
# dp
self.dp_torque_apply_length = 1.5 # secs of torque we apply for
self.dp_lc_auto_start = 0. # time to start alc
self.dp_lc_auto_start_in = 0. # remaining time to start alc
self.dp_lc_auto_torque_end = 0. # time to end applying torque
self.dp_torque_apply = False # should we apply torque?
self.laneless_mode = 2 # AUTO
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
def reset_mpc(self, x0=np.zeros(6)):
self.x0 = x0
self.lat_mpc.reset(x0=self.x0)
self.desired_curvature = 0.0
self.safe_desired_curvature = 0.0
self.desired_curvature_rate = 0.0
self.safe_desired_curvature_rate = 0.0
def update(self, sm, CP):
self.use_lanelines = not sm['dragonConf'].dpLaneLessModeCtrl
self.laneless_mode = sm['dragonConf'].dpLaneLessMode
v_ego = sm['carState'].vEgo
active = sm['controlsState'].active
measured_curvature = sm['controlsState'].curvature
self.LP.update_dp_set_offsets(sm['dragonConf'].dpCameraOffset,
sm['dragonConf'].dpPathOffset)
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 < (sm['dragonConf'].dpLcMinMph *
CV.MPH_TO_MS)
if (not active) or (self.lane_change_timer > LANE_CHANGE_TIME_MAX):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
reset = False
if one_blinker:
cur_time = sec_since_boot()
# reach auto lc condition
if not below_lane_change_speed and sm[
'dragonConf'].dpLateralMode == 2 and v_ego >= (
sm['dragonConf'].dpLcAutoMinMph * CV.MPH_TO_MS):
# work out alc start time and torque apply end time
if self.dp_lc_auto_start == 0.:
self.dp_lc_auto_start = cur_time + sm[
'dragonConf'].dpLcAutoDelay
self.dp_lc_auto_torque_end = self.dp_lc_auto_start + self.dp_torque_apply_length
else:
# work out how long til alc start
# for display only
self.dp_lc_auto_start_in = self.dp_lc_auto_start - cur_time
self.dp_torque_apply = True if self.dp_lc_auto_start < cur_time <= self.dp_lc_auto_torque_end else False
else:
reset = True
# reset all vals
if not active or reset:
self.dp_lc_auto_start = 0.
self.dp_lc_auto_start_in = 0.
self.dp_lc_auto_torque_end = 0.
self.dp_torque_apply = False
# LaneChangeState.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
# LaneChangeState.preLaneChange
elif self.lane_change_state == LaneChangeState.preLaneChange:
# Set lane change direction
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
else: # If there are no blinkers we will go back to LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
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))
# if human made lane change prior alca, we should stop alca until new blinker (off -> on)
self.dp_lc_auto_start = self.dp_lc_auto_torque_end if torque_applied else self.dp_lc_auto_start
torque_applied = self.dp_torque_apply if self.dp_torque_apply else torque_applied
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
# 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
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
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 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]
# 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
self.d_path_w_lines_xyz = self.LP.get_d_path(v_ego, self.t_idxs,
self.path_xyz)
if self.use_lanelines:
d_path_xyz = self.d_path_w_lines_xyz
self.lat_mpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.laneless_mode_status = False
elif self.laneless_mode == 0:
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,
CP.steerRateCost)
self.laneless_mode_status = False
elif self.laneless_mode == 1:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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, CP.steerRateCost)
self.laneless_mode_status = True
elif self.laneless_mode == 2 and (
(self.LP.lll_prob + self.LP.rll_prob) / 2 <
0.3) and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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, CP.steerRateCost)
self.laneless_mode_status = True
self.laneless_mode_status_buffer = True
elif self.laneless_mode == 2 and ((self.LP.lll_prob + self.LP.rll_prob)/2 > 0.5) and \
self.laneless_mode_status_buffer and self.lane_change_state == LaneChangeState.off:
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,
CP.steerRateCost)
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
elif self.laneless_mode == 2 and self.laneless_mode_status_buffer == True and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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, CP.steerRateCost)
self.laneless_mode_status = True
else:
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,
CP.steerRateCost)
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
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
self.lat_mpc.run(self.x0, v_ego, CAR_ROTATION_RADIUS, 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 infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.lat_mpc.x_sol[:, 3])
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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=[
'carState', 'controlsState', 'modelV2', 'dragonConf'
])
plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
plan_send.lateralPlan.psis = [
float(x) for x in self.lat_mpc.x_sol[0:CONTROL_N, 2]
]
plan_send.lateralPlan.curvatures = [
float(x) for x in self.lat_mpc.x_sol[0:CONTROL_N, 3]
]
plan_send.lateralPlan.curvatureRates = [
float(x) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]
] + [0.0]
plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
plan_send.lateralPlan.dProb = float(self.LP.d_prob)
plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.lateralPlan.desire = self.desire
plan_send.lateralPlan.useLaneLines = self.use_lanelines
plan_send.lateralPlan.laneChangeState = self.lane_change_state
plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
# dp
plan_send.lateralPlan.dpLaneLessModeStatus = bool(
self.laneless_mode_status)
plan_send.lateralPlan.dpALCAStartIn = self.dp_lc_auto_start_in
plan_send.lateralPlan.dPathWLinesX = [
float(x) for x in self.d_path_w_lines_xyz[:, 0]
]
plan_send.lateralPlan.dPathWLinesY = [
float(y) for y in self.d_path_w_lines_xyz[:, 1]
]
pm.send('lateralPlan', plan_send)
class LateralPlanner:
def __init__(self, CP, use_lanelines=True, wide_camera=False):
self.use_lanelines = use_lanelines
self.wide_camera = wide_camera
self.LP = LanePlanner(wide_camera)
self.last_cloudlog_t = 0
self.solution_invalid_cnt = 0
self.lane_change_enabled = Params().get_bool('LaneChangeEnabled')
self.auto_lane_change_enabled = Params().get_bool('AutoLaneChangeEnabled')
self.auto_lane_change_timer = 0.0
self.prev_torque_applied = False
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.keep_pulse_timer = 0.0
self.prev_one_blinker = False
self.desire = log.LateralPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.path_xyz_stds = np.ones((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE,))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
self.lat_mpc = LateralMpc()
self.reset_mpc(np.zeros(4))
def reset_mpc(self, x0=np.zeros(4)):
self.x0 = x0
self.lat_mpc.reset(x0=self.x0)
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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=['carState', 'controlsState', 'modelV2'])
lateralPlan = plan_send.lateralPlan
lateralPlan.laneWidth = float(self.LP.lane_width)
lateralPlan.dPathPoints = self.y_pts.tolist()
lateralPlan.psis = self.lat_mpc.x_sol[0:CONTROL_N, 2].tolist()
lateralPlan.curvatures = self.lat_mpc.x_sol[0:CONTROL_N, 3].tolist()
lateralPlan.curvatureRates = [float(x) for x in self.lat_mpc.u_sol[0:CONTROL_N - 1]] + [0.0]
lateralPlan.lProb = float(self.LP.lll_prob)
lateralPlan.rProb = float(self.LP.rll_prob)
lateralPlan.dProb = float(self.LP.d_prob)
lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
lateralPlan.solverExecutionTime = self.lat_mpc.solve_time
lateralPlan.desire = self.desire
lateralPlan.useLaneLines = self.use_lanelines
lateralPlan.laneChangeState = self.lane_change_state
lateralPlan.laneChangeDirection = self.lane_change_direction
lateralPlan.autoLaneChangeEnabled = self.auto_lane_change_enabled
lateralPlan.autoLaneChangeTimer = int(AUTO_LCA_START_TIME) - int(self.auto_lane_change_timer)
pm.send('lateralPlan', plan_send)
class LateralPlanner():
def __init__(self, CP, use_lanelines=True, wide_camera=False):
self.use_lanelines = use_lanelines
self.LP = LanePlanner(wide_camera)
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.setup_mpc()
self.solution_invalid_cnt = 0
self.laneless_mode = int(Params().get("LanelessMode", encoding="utf8"))
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
self.lane_change_delay = int(Params().get("OpkrAutoLaneChangeDelay",
encoding="utf8"))
self.lane_change_auto_delay = 0.0 if self.lane_change_delay == 0 else 0.2 if self.lane_change_delay == 1 else 0.5 if self.lane_change_delay == 2 \
else 1.0 if self.lane_change_delay == 3 else 1.5 if self.lane_change_delay == 4 else 2.0
self.lane_change_wait_timer = 0.0
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
self.desire = log.LateralPlan.Desire.none
self.path_xyz = np.zeros((TRAJECTORY_SIZE, 3))
self.path_xyz_stds = np.ones((TRAJECTORY_SIZE, 3))
self.plan_yaw = np.zeros((TRAJECTORY_SIZE, ))
self.t_idxs = np.arange(TRAJECTORY_SIZE)
self.y_pts = np.zeros(TRAJECTORY_SIZE)
self.lane_change_adjust = [
float(
Decimal(Params().get("LCTimingFactor30", encoding="utf8")) *
Decimal('0.01')),
float(
Decimal(Params().get("LCTimingFactor60", encoding="utf8")) *
Decimal('0.01')),
float(
Decimal(Params().get("LCTimingFactor80", encoding="utf8")) *
Decimal('0.01')),
float(
Decimal(Params().get("LCTimingFactor110", encoding="utf8")) *
Decimal('0.01'))
]
self.lane_change_adjust_vel = [
30 * CV.KPH_TO_MS, 60 * CV.KPH_TO_MS, 80 * CV.KPH_TO_MS,
110 * CV.KPH_TO_MS
]
self.lane_change_adjust_new = 2
self.lane_change_adjust_enable = Params().get_bool(
"LCTimingFactorEnable")
self.standstill_elapsed_time = 0.0
self.v_cruise_kph = 0
self.stand_still = False
self.output_scale = 0.0
self.second = 0.0
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init()
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].curvature = 0.0
self.desired_curvature = 0.0
self.safe_desired_curvature = 0.0
self.desired_curvature_rate = 0.0
self.safe_desired_curvature_rate = 0.0
def update(self, sm, CP):
self.second += DT_MDL
if self.second > 1.0:
self.use_lanelines = not Params().get_bool("EndToEndToggle")
self.laneless_mode = int(Params().get("LanelessMode",
encoding="utf8"))
self.second = 0.0
self.v_cruise_kph = sm['controlsState'].vCruise
self.stand_still = sm['carState'].standStill
try:
if CP.lateralTuning.which() == 'pid':
self.output_scale = sm[
'controlsState'].lateralControlState.pidState.output
elif CP.lateralTuning.which() == 'indi':
self.output_scale = sm[
'controlsState'].lateralControlState.indiState.output
elif CP.lateralTuning.which() == 'lqr':
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)
if len(md.orientation.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 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) >=
(CP.steerMaxV[0] - 0.15) 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))
# LaneChangeState.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
if self.lane_change_adjust_enable:
self.lane_change_adjust_new = interp(
v_ego, self.lane_change_adjust_vel,
self.lane_change_adjust)
# LaneChangeState.preLaneChange
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
# LaneChangeState.laneChangeStarting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .5s
self.lane_change_ll_prob = max(
self.lane_change_ll_prob -
self.lane_change_adjust_new * DT_MDL, 0.0)
# 98% certainty
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
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 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
if self.use_lanelines:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.laneless_mode_status = False
elif self.laneless_mode == 0:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.laneless_mode_status = False
elif self.laneless_mode == 1:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
elif self.laneless_mode == 2 and (
(self.LP.lll_prob + self.LP.rll_prob) / 2 <
0.3) and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
self.laneless_mode_status_buffer = True
elif self.laneless_mode == 2 and ((self.LP.lll_prob + self.LP.rll_prob)/2 > 0.5) and \
self.laneless_mode_status_buffer and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
elif self.laneless_mode == 2 and self.laneless_mode_status_buffer == True and self.lane_change_state == LaneChangeState.off:
d_path_xyz = self.path_xyz
path_cost = np.clip(
abs(self.path_xyz[0, 1] / self.path_xyz_stds[0, 1]), 0.5,
5.0) * 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.libmpc.set_weights(path_cost, heading_cost, CP.steerRateCost)
self.laneless_mode_status = True
else:
d_path_xyz = self.LP.get_d_path(v_ego, self.t_idxs, self.path_xyz)
self.libmpc.set_weights(MPC_COST_LAT.PATH, MPC_COST_LAT.HEADING,
CP.steerRateCost)
self.laneless_mode_status = False
self.laneless_mode_status_buffer = False
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
# for now CAR_ROTATION_RADIUS is disabled
# to use it, enable it in the MPC
assert abs(CAR_ROTATION_RADIUS) < 1e-3
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[:LAT_MPC_N + 1],
self.mpc_solution.curvature)
# 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()
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 publish(self, sm, pm):
plan_solution_valid = self.solution_invalid_cnt < 2
plan_send = messaging.new_message('lateralPlan')
plan_send.valid = sm.all_alive_and_valid(
service_list=['carState', 'controlsState', 'modelV2'])
plan_send.lateralPlan.laneWidth = float(self.LP.lane_width)
plan_send.lateralPlan.dPathPoints = [float(x) for x in self.y_pts]
plan_send.lateralPlan.psis = [
float(x) for x in self.mpc_solution.psi[0:CONTROL_N]
]
plan_send.lateralPlan.curvatures = [
float(x) for x in self.mpc_solution.curvature[0:CONTROL_N]
]
plan_send.lateralPlan.curvatureRates = [
float(x) for x in self.mpc_solution.curvature_rate[0:CONTROL_N - 1]
] + [0.0]
plan_send.lateralPlan.lProb = float(self.LP.lll_prob)
plan_send.lateralPlan.rProb = float(self.LP.rll_prob)
plan_send.lateralPlan.dProb = float(self.LP.d_prob)
plan_send.lateralPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.lateralPlan.desire = self.desire
plan_send.lateralPlan.laneChangeState = self.lane_change_state
plan_send.lateralPlan.laneChangeDirection = self.lane_change_direction
plan_send.lateralPlan.steerRateCost = float(self.steer_rate_cost)
plan_send.lateralPlan.outputScale = float(self.output_scale)
plan_send.lateralPlan.vCruiseSet = float(self.v_cruise_kph)
plan_send.lateralPlan.vCurvature = float(sm['controlsState'].curvature)
plan_send.lateralPlan.lanelessMode = bool(self.laneless_mode_status)
if self.stand_still:
self.standstill_elapsed_time += DT_MDL
else:
self.standstill_elapsed_time = 0.0
plan_send.lateralPlan.standstillElapsedTime = int(
self.standstill_elapsed_time)
pm.send('lateralPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution.x)
dat.liveMpc.y = list(self.mpc_solution.y)
dat.liveMpc.psi = list(self.mpc_solution.psi)
dat.liveMpc.curvature = list(self.mpc_solution.curvature)
dat.liveMpc.cost = self.mpc_solution.cost
pm.send('liveMpc', dat)
