class PathPlanner():
def __init__(self, CP):
self.LP = LanePlanner()
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.steerRatio = CP.steerRatio
self.setup_mpc()
self.solution_invalid_cnt = 0
self.steerRatio_last = 0
self.params = Params()
# Lane change
self.lane_change_enabled = self.params.get('LaneChangeEnabled') == b'1'
self.lane_change_auto_delay = self.params.get_OpkrAutoLanechangedelay(
) #int( self.params.get('OpkrAutoLanechangedelay') )
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_run_timer = 0.0
self.lane_change_wait_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
# atom
self.trPATH = trace1.Loger("path")
self.trLearner = trace1.Loger("Learner")
self.trpathPlan = trace1.Loger("pathPlan")
self.atom_timer_cnt = 0
self.atom_steer_ratio = None
self.atom_sr_boost_bp = [0., 0.]
self.atom_sr_boost_range = [0., 0.]
self.carParams_valid = False
self.m_avg = ma.MoveAvg()
def limit_ctrl(self, value, limit, offset):
p_limit = offset + limit
m_limit = offset - limit
if value > p_limit:
value = p_limit
elif value < m_limit:
value = m_limit
return value
def limit_ctrl1(self, value, limit1, limit2, offset):
p_limit = offset + limit1
m_limit = offset - limit2
if value > p_limit:
value = p_limit
elif value < m_limit:
value = m_limit
return value
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
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].delta = 0.0
self.angle_steers_des = 0.0
self.angle_steers_des_mpc = 0.0
self.angle_steers_des_prev = 0.0
self.angle_steers_des_time = 0.0
def atom_tune(self, v_ego_kph, sr_value, atomTuning): # 조향각에 따른 변화.
self.sr_KPH = atomTuning.sRKPH
self.sr_BPV = atomTuning.sRBPV
self.sr_steerRatioV = atomTuning.sRsteerRatioV
self.sr_SteerRatio = []
nPos = 0
for steerRatio in self.sr_BPV: # steerRatio
self.sr_SteerRatio.append(
interp(sr_value, steerRatio, self.sr_steerRatioV[nPos]))
nPos += 1
if nPos > 20:
break
steerRatio = interp(v_ego_kph, self.sr_KPH, self.sr_SteerRatio)
return steerRatio
def atom_actuatorDelay(self, v_ego_kph, sr_value, atomTuning):
self.sr_KPH = atomTuning.sRKPH
self.sr_BPV = atomTuning.sRBPV
self.sr_ActuatorDelayV = atomTuning.sRsteerActuatorDelayV
self.sr_ActuatorDelay = []
nPos = 0
for steerRatio in self.sr_BPV:
self.sr_ActuatorDelay.append(
interp(sr_value, steerRatio, self.sr_ActuatorDelayV[nPos]))
nPos += 1
if nPos > 10:
break
actuatorDelay = interp(v_ego_kph, self.sr_KPH, self.sr_ActuatorDelay)
return actuatorDelay
def atom_steer(self, sr_value, sr_up, sr_dn):
delta = sr_value - self.steerRatio_last
sr_up = min(abs(delta), sr_up)
sr_dn = min(abs(delta), sr_dn)
steerRatio = self.steerRatio_last
if delta > 0:
steerRatio += sr_up
elif delta < 0:
steerRatio -= sr_dn
self.steerRatio_last = steerRatio
return steerRatio
def update(self, sm, pm, CP, VM):
self.atom_timer_cnt += 1
if self.atom_timer_cnt > 1000:
self.atom_timer_cnt = 0
cruiseState = sm['carState'].cruiseState
leftBlindspot = sm['carState'].leftBlindspot
rightBlindspot = sm['carState'].rightBlindspot
lateralsRatom = CP.lateralsRatom
atomTuning = CP.atomTuning
#if atomTuning is None or lateralsRatom is None:
#print('carparams={} steerRatio={} carParams_valid={}'.format(sm.updated['carParams'], sm['carParams'].steerRatio, self.carParams_valid ) )
if not self.carParams_valid and sm[
'carParams'].steerRatio: # sm.updated['carParams']:
self.carParams_valid = True
if self.carParams_valid:
lateralsRatom = sm['carParams'].lateralsRatom
atomTuning = sm['carParams'].atomTuning
v_ego = sm['carState'].vEgo
angle_steers = sm['carState'].steeringAngle
steeringPressed = sm['carState'].steeringPressed
steeringTorque = sm['carState'].steeringTorque
active = sm['controlsState'].active
model_sum = sm['controlsState'].modelSum
v_ego_kph = v_ego * CV.MS_TO_KPH
self.steerRatio = sm['liveParameters'].steerRatio
angle_offset = sm['liveParameters'].angleOffset
angleOffsetAverage = sm['liveParameters'].angleOffsetAverage
stiffnessFactor = sm['liveParameters'].stiffnessFactor
#if (self.atom_timer_cnt % 100) == 0:
# str_log3 = 'angleOffset={:.1f} angleOffsetAverage={:.3f} steerRatio={:.2f} stiffnessFactor={:.3f} '.format( angle_offset, angleOffsetAverage, self.steerRatio, stiffnessFactor )
# self.trLearner.add( 'LearnerParam {} carParams={}'.format( str_log3, self.carParams_valid ) )
if lateralsRatom.learnerParams:
pass
else:
# atom
if self.carParams_valid:
self.steer_rate_cost = sm['carParams'].steerRateCost
self.steerRatio = sm['carParams'].steerRatio
else:
self.steer_rate_cost = CP.steerRateCost
self.steerRatio = CP.steerRatio
#xp = [-5,0,5]
#fp = [0.4, 0.7, 0.4]
#self.steer_rate_cost = interp( angle_steers, xp, fp )
steerRatio = self.atom_tune(v_ego_kph, angle_steers, atomTuning)
self.steerRatio = self.atom_steer(steerRatio, 2, 1)
#actuatorDelay = CP.steerActuatorDelay
steerActuatorDelay = self.atom_actuatorDelay(v_ego_kph, angle_steers,
atomTuning)
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
VM.update_params(stiffnessFactor, self.steerRatio)
curvature_factor = VM.curvature_factor(v_ego)
self.LP.parse_model(sm['model'])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_run_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:
l_poly = self.LP.l_poly[3]
r_poly = self.LP.r_poly[3]
c_prob = l_poly + r_poly
torque_applied = steeringPressed and \
((steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or \
(steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
blindspot_detected = (
(leftBlindspot
and self.lane_change_direction == LaneChangeDirection.left) or
(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 cruiseState.cruiseSwState == Buttons.CANCEL:
self.lane_change_state = LaneChangeState.off
self.lane_change_ll_prob = 1.0
self.lane_change_wait_timer = 0
elif 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
xp = [40, 50, 60, 70]
fp2 = [0.2, 0.6, 1.2, 1.5]
lane_time = interp(v_ego_kph, xp, fp2)
self.lane_change_ll_prob = max(
self.lane_change_ll_prob - lane_time * 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 self.lane_change_ll_prob > 0.99 and abs(c_prob) < 0.3:
self.lane_change_state = LaneChangeState.laneChangeDone
# done
elif self.lane_change_state == LaneChangeState.laneChangeDone:
if not one_blinker:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [
LaneChangeState.off, LaneChangeState.preLaneChange
]:
self.lane_change_run_timer = 0.0
else:
self.lane_change_run_timer += DT_MDL
self.prev_one_blinker = one_blinker
desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Turn off lanes during lane change
if desire == log.PathPlan.Desire.laneChangeRight or desire == log.PathPlan.Desire.laneChangeLeft:
self.LP.l_prob *= self.lane_change_ll_prob
self.LP.r_prob *= self.lane_change_ll_prob
self.LP.update_d_poly(v_ego, lateralsRatom.cameraOffset)
# account for actuation delay
self.cur_state = calc_states_after_delay(self.cur_state, v_ego,
angle_steers - angle_offset,
curvature_factor, VM.sR,
steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly),
list(self.LP.d_poly), self.LP.l_prob,
self.LP.r_prob, curvature_factor, v_ego_mpc,
self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
rate_desired = math.degrees(self.mpc_solution[0].rate[0] * VM.sR)
else:
delta_desired = math.radians(angle_steers - angle_offset) / VM.sR
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * VM.sR) + angle_offset)
org_angle_steers_des = self.angle_steers_des_mpc
# atom
if steeringPressed:
delta_steer = org_angle_steers_des - angle_steers
xp = [-255, 0, 255]
fp2 = [5, 0, 5]
limit_steers = interp(steeringTorque, xp, fp2)
if steeringTorque < 0: # right
if delta_steer > 0:
self.angle_steers_des_mpc = self.limit_ctrl(
org_angle_steers_des, limit_steers, angle_steers)
elif steeringTorque > 0: # left
if delta_steer < 0:
self.angle_steers_des_mpc = self.limit_ctrl(
org_angle_steers_des, limit_steers, angle_steers)
elif v_ego_kph < 15: # 30
xp = [3, 10, 15]
fp2 = [3, 5, 7]
limit_steers = interp(v_ego_kph, xp, fp2)
self.angle_steers_des_mpc = self.limit_ctrl(
org_angle_steers_des, limit_steers, angle_steers)
elif v_ego_kph > 60:
pass
elif abs(angle_steers) > 10: # angle steer > 10
# 2.
xp = [-10, -5, 0, 5, 10] # 5 10=>28 15=>35, 30=>52
fp1 = [3, 8, 10, 20, 10] # +
fp2 = [10, 20, 10, 8, 3] # -
limit_steers1 = interp(model_sum, xp, fp1) # +
limit_steers2 = interp(model_sum, xp, fp2) # -
self.angle_steers_des_mpc = self.limit_ctrl1(
org_angle_steers_des, limit_steers1, limit_steers2,
angle_steers)
delta_steer = self.angle_steers_des_mpc - angle_steers
ANGLE_LIMIT = 8
if delta_steer > ANGLE_LIMIT:
p_angle_steers = angle_steers + ANGLE_LIMIT
self.angle_steers_des_mpc = p_angle_steers
elif delta_steer < -ANGLE_LIMIT:
m_angle_steers = angle_steers - ANGLE_LIMIT
self.angle_steers_des_mpc = m_angle_steers
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, self.steer_rate_cost)
self.cur_state[0].delta = math.radians(angle_steers -
angle_offset) / VM.sR
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
#self.trPATH.add( 'mpc_nans ={} libmpc steer_rate_cost={} delta={} angle_steers={}'.format( mpc_nans, self.steer_rate_cost, self.cur_state[0].delta, angle_steers ) )
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 3
plan_send = messaging.new_message('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=[
'carState', 'controlsState', 'liveParameters', 'model'
])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
plan_send.pathPlan.angleOffset = float(angleOffsetAverage)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.desire = desire
plan_send.pathPlan.laneChangeState = self.lane_change_state
plan_send.pathPlan.laneChangeDirection = self.lane_change_direction
plan_send.pathPlan.steerRatio = self.steerRatio
plan_send.pathPlan.steerActuatorDelay = steerActuatorDelay
pm.send('pathPlan', plan_send)
#if self.solution_invalid_cnt > 0:
# str_log3 = 'v_ego_kph={:.1f} angle_steers_des_mpc={:.1f} angle_steers={:.1f} solution_invalid_cnt={:.0f} mpc_solution={:.1f}/{:.0f}'.format( v_ego_kph, self.angle_steers_des_mpc, angle_steers, self.solution_invalid_cnt, self.mpc_solution[0].cost, mpc_nans )
# self.trpathPlan.add( 'pathPlan {} LOG_MPC={}'.format( str_log3, LOG_MPC ) )
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
class PathPlanner():
def __init__(self, CP):
self.LP = LanePlanner()
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.steerRatio = CP.steerRatio
self.setup_mpc()
self.solution_invalid_cnt = 0
self.params = Params()
# Lane change
self.lane_change_enabled = self.params.get('LaneChangeEnabled') == b'1'
self.lane_change_auto_delay = self.params.get_OpkrAutoLanechangedelay(
) #int( self.params.get('OpkrAutoLanechangedelay') )
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_run_timer = 0.0
self.lane_change_wait_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
# atom
self.trPATH = trace1.Loger("path")
self.trLearner = trace1.Loger("Learner")
self.trpathPlan = trace1.Loger("pathPlan")
self.atom_timer_cnt = 0
self.atom_steer_ratio = None
self.atom_sr_boost_bp = [0., 0.]
self.atom_sr_boost_range = [0., 0.]
def limit_ctrl(self, value, limit, offset):
p_limit = offset + limit
m_limit = offset - limit
if value > p_limit:
value = p_limit
elif value < m_limit:
value = m_limit
return value
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
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].delta = 0.0
self.angle_steers_des = 0.0
self.angle_steers_des_mpc = 0.0
self.angle_steers_des_prev = 0.0
self.angle_steers_des_time = 0.0
def update(self, sm, pm, CP, VM):
self.atom_timer_cnt += 1
if self.atom_timer_cnt > 1000:
self.atom_timer_cnt = 0
leftBlindspot = sm['carState'].leftBlindspot
rightBlindspot = sm['carState'].rightBlindspot
v_ego = sm['carState'].vEgo
angle_steers = sm['carState'].steeringAngle
steeringPressed = sm['carState'].steeringPressed
steeringTorque = sm['carState'].steeringTorque
active = sm['controlsState'].active
v_ego_kph = v_ego * CV.MS_TO_KPH
self.steerRatio = sm['liveParameters'].steerRatio
angle_offset = sm['liveParameters'].angleOffset
angleOffsetAverage = sm['liveParameters'].angleOffsetAverage
stiffnessFactor = sm['liveParameters'].stiffnessFactor
if (self.atom_timer_cnt % 100) == 0:
str_log3 = 'angleOffset={:.1f} angleOffsetAverage={:.3f} steerRatio={:.2f} stiffnessFactor={:.3f} '.format(
angle_offset, angleOffsetAverage, self.steerRatio,
stiffnessFactor)
self.trLearner.add('LearnerParam {}'.format(str_log3))
if CP.lateralsRatom.learnerParams:
pass
else:
#angle_offset = 0
#angleOffsetAverage = 0
# atom
self.steer_rate_cost = sm['carParams'].steerRateCost
self.steerRatio = sm['carParams'].steerRatio
if self.steer_rate_cost == 0:
self.steer_rate_cost = CP.steerRateCost
if self.steerRatio == 0:
self.steerRatio = CP.steerRatio
self.steerRatio = interp(angle_steers, CP.atomTuning.sRBPV,
CP.atomTuning.sRsteerRatioV)
str_log1 = 'steerRatio={:.1f}/{:.1f} bp={} range={}'.format(
self.steerRatio, CP.steerRatio, CP.atomTuning.sRBPV,
CP.atomTuning.sRsteerRatioV)
str_log2 = 'steerRateCost={:.2f}'.format(self.steer_rate_cost)
self.trPATH.add('{} {}'.format(str_log1, str_log2))
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
VM.update_params(stiffnessFactor,
self.steerRatio) # sm['liveParameters'].steerRatio)
curvature_factor = VM.curvature_factor(v_ego)
self.LP.parse_model(sm['model'])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_run_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 = steeringPressed and \
((steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or \
(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_ll_prob = max(
self.lane_change_ll_prob - 1.5 * 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 self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.laneChangeDone
# done
elif self.lane_change_state == LaneChangeState.laneChangeDone:
if not one_blinker:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [
LaneChangeState.off, LaneChangeState.preLaneChange
]:
self.lane_change_run_timer = 0.0
else:
self.lane_change_run_timer += DT_MDL
self.prev_one_blinker = one_blinker
desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Turn off lanes during lane change
if desire == log.PathPlan.Desire.laneChangeRight or desire == log.PathPlan.Desire.laneChangeLeft:
self.LP.l_prob *= self.lane_change_ll_prob
self.LP.r_prob *= self.lane_change_ll_prob
self.LP.update_d_poly(v_ego)
# account for actuation delay
self.cur_state = calc_states_after_delay(self.cur_state, v_ego,
angle_steers - angle_offset,
curvature_factor, VM.sR,
CP.steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly),
list(self.LP.d_poly), self.LP.l_prob,
self.LP.r_prob, curvature_factor, v_ego_mpc,
self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
rate_desired = math.degrees(self.mpc_solution[0].rate[0] * VM.sR)
else:
delta_desired = math.radians(angle_steers - angle_offset) / VM.sR
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * VM.sR) + angle_offset)
org_angle_steers_des = self.angle_steers_des_mpc
# atom
if steeringPressed:
delta_steer = self.angle_steers_des_mpc - angle_steers
xp = [-255, 0, 255]
fp2 = [5, 0, 5]
limit_steers = interp(steeringTorque, xp, fp2)
if steeringTorque < 0: # right
if delta_steer > 0:
self.angle_steers_des_mpc = self.limit_ctrl(
org_angle_steers_des, limit_steers, angle_steers)
elif steeringTorque > 0: # left
if delta_steer < 0:
self.angle_steers_des_mpc = self.limit_ctrl(
org_angle_steers_des, limit_steers, angle_steers)
elif v_ego_kph < 30:
xp = [5, 15, 30]
fp2 = [3, 5, 9]
limit_steers = interp(v_ego_kph, xp, fp2)
self.angle_steers_des_mpc = self.limit_ctrl(
org_angle_steers_des, limit_steers, angle_steers)
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, self.steer_rate_cost)
self.cur_state[0].delta = math.radians(angle_steers -
angle_offset) / VM.sR
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 3
plan_send = messaging.new_message('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=[
'carState', 'controlsState', 'liveParameters', 'model'
])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
plan_send.pathPlan.angleOffset = float(angleOffsetAverage)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.desire = desire
plan_send.pathPlan.laneChangeState = self.lane_change_state
plan_send.pathPlan.laneChangeDirection = self.lane_change_direction
pm.send('pathPlan', plan_send)
if self.solution_invalid_cnt > 0:
str_log3 = 'v_ego_kph={:.1f} angle_steers_des_mpc={:.1f} angle_steers={:.1f} solution_invalid_cnt={:.0f} mpc_solution={:.1f}/{:.0f}'.format(
v_ego_kph, self.angle_steers_des_mpc, angle_steers,
self.solution_invalid_cnt, self.mpc_solution[0].cost, mpc_nans)
self.trpathPlan.add('pathPlan {}'.format(str_log3))
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
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.params = Params()
kyd = kyd_conf(CP)
if kyd.conf['steerRatio'] == "-1":
self.steerRatio = CP.steerRatio
else:
self.steerRatio = float(kyd.conf['steerRatio'])
if kyd.conf['steerRateCost'] == "-1":
self.steer_rate_cost = CP.steerRateCost
else:
self.steer_rate_cost = float(kyd.conf['steerRateCost'])
self.kyd_steerRatio = None
self.sRBP = [0., 0.]
self.sRBoost = [0., 0.]
# Lane change
self.lane_change_enabled = self.params.get('LaneChangeEnabled') == b'1'
self.lane_change_auto_delay = self.params.get_OpkrAutoLanechangedelay(
) #int( self.params.get('OpkrAutoLanechangedelay') )
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_run_timer = 0.0
self.lane_change_wait_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
self.param_OpkrEnableLearner = int(
self.params.get('OpkrEnableLearner'))
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
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].delta = 0.0
self.angle_steers_des = 0.0
self.angle_steers_des_mpc = 0.0
self.angle_steers_des_prev = 0.0
self.angle_steers_des_time = 0.0
def update(self, sm, pm, CP, VM):
v_ego = sm['carState'].vEgo
angle_steers = sm['carState'].steeringAngle
active = sm['controlsState'].active
angle_offset = sm['liveParameters'].angleOffset
if not self.param_OpkrEnableLearner:
kyd = kyd_conf()
#self.steer_rate_cost = float(kyd.conf['steerRateCost'])
self.sRBP = kyd.conf['sR_BP']
self.sRBoost = kyd.conf['sR_Boost']
boost_rate = interp(abs(angle_steers), self.sRBP, self.sRBoost)
self.kyd_steerRatio = self.steerRatio + boost_rate
self.sR_Cost = [
1.0, 0.90, 0.81, 0.73, 0.66, 0.60, 0.54, 0.48, 0.36, 0.275,
0.20, 0.175, 0.15, 0.11, 0.05
]
#self.sR_Cost = [0.75,0.67,0.60,0.54,0.48,0.425,0.37,0.32,0.24,0.19,0.15,0.14,0.13,0.11,0.05]
#self.sR_Cost = [0.50,0.46,0.425,0.395,0.37,0.34,0.315,0.29,0.23,0.185,0.15,0.14,0.13,0.11,0.05]
#steerRatio = 10.0
#"sR_BP": [0.0,2.0,4.0,6.0,8.0,10.0,12.0,14.0,20.0,27.0,35.0,40.0,45.0,60.0,100],
#"sR_Boost": [0.0,0.7,1.3,1.9,2.5,3.05,3.55,4.0,5.0,5.7,6.2,6.35,6.4,6.5,8.0],
self.steer_rate_cost = interp(abs(angle_steers), self.sRBP,
self.sR_Cost)
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
# Update vehicle model
x = max(sm['liveParameters'].stiffnessFactor, 0.1)
sr = max(sm['liveParameters'].steerRatio, 0.1)
VM.update_params(x, sr)
curvature_factor = VM.curvature_factor(v_ego)
self.LP.parse_model(sm['model'])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_run_timer >
LANE_CHANGE_TIME_MAX) or (not one_blinker) or (
not self.lane_change_enabled):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
torque_applied = sm['carState'].steeringPressed and \
((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
blindspot_detected = (
(sm['carState'].leftBlindspot
and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].rightBlindspot
and self.lane_change_direction == LaneChangeDirection.right))
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
self.lane_change_state = LaneChangeState.preLaneChange
self.lane_change_ll_prob = 1.0
self.lane_change_wait_timer = 0
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
self.lane_change_wait_timer += DT_MDL
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
elif not blindspot_detected and (
torque_applied or (self.lane_change_auto_delay
and self.lane_change_wait_timer >
self.lane_change_auto_delay)):
self.lane_change_state = LaneChangeState.laneChangeStarting
# starting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .5s
self.lane_change_ll_prob = max(
self.lane_change_ll_prob - 1.5 * DT_MDL, 0.0)
# 98% certainty
if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01:
self.lane_change_state = LaneChangeState.laneChangeFinishing
# finishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
# fade in laneline over 1s
self.lane_change_ll_prob = min(
self.lane_change_ll_prob + DT_MDL, 1.0)
if one_blinker and self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.laneChangeDone
# done
elif self.lane_change_state == LaneChangeState.laneChangeDone:
if not one_blinker:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [
LaneChangeState.off, LaneChangeState.preLaneChange
]:
self.lane_change_run_timer = 0.0
else:
self.lane_change_run_timer += DT_MDL
self.prev_one_blinker = one_blinker
desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Turn off lanes during lane change
if desire == log.PathPlan.Desire.laneChangeRight or desire == log.PathPlan.Desire.laneChangeLeft:
self.LP.l_prob *= self.lane_change_ll_prob
self.LP.r_prob *= self.lane_change_ll_prob
self.LP.update_d_poly(v_ego)
# account for actuation delay
if self.param_OpkrEnableLearner:
self.cur_state = calc_states_after_delay(
self.cur_state, v_ego, angle_steers - angle_offset,
curvature_factor, VM.sR, CP.steerActuatorDelay)
else:
self.cur_state = calc_states_after_delay(
self.cur_state, v_ego, angle_steers - angle_offset,
curvature_factor, self.kyd_steerRatio, CP.steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly),
list(self.LP.d_poly), self.LP.l_prob,
self.LP.r_prob, curvature_factor, v_ego_mpc,
self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
if self.param_OpkrEnableLearner:
rate_desired = math.degrees(self.mpc_solution[0].rate[0] *
VM.sR)
else:
rate_desired = math.degrees(self.mpc_solution[0].rate[0] *
self.kyd_steerRatio)
else:
if self.param_OpkrEnableLearner:
delta_desired = math.radians(angle_steers -
angle_offset) / VM.sR
else:
delta_desired = math.radians(
angle_steers - angle_offset) / self.kyd_steerRatio
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
if self.param_OpkrEnableLearner:
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * VM.sR) + angle_offset)
else:
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * self.kyd_steerRatio) +
angle_offset)
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, self.steer_rate_cost)
if self.param_OpkrEnableLearner:
self.cur_state[0].delta = math.radians(angle_steers -
angle_offset) / VM.sR
else:
self.cur_state[0].delta = math.radians(
angle_steers - angle_offset) / self.kyd_steerRatio
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 3
plan_send = messaging.new_message('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=[
'carState', 'controlsState', 'liveParameters', 'model'
])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
plan_send.pathPlan.angleOffset = float(
sm['liveParameters'].angleOffsetAverage)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.desire = desire
plan_send.pathPlan.laneChangeState = self.lane_change_state
plan_send.pathPlan.laneChangeDirection = self.lane_change_direction
if not self.param_OpkrEnableLearner:
plan_send.pathPlan.steerRatio = float(self.kyd_steerRatio)
pm.send('pathPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
class PathPlanner():
def __init__(self, CP):
self.PathPlan = trace1.Loger("077_R3_LQR_parhplan")
self.LP = LanePlanner()
self.last_cloudlog_t = 0
self.steer_rate_cost = CP.steerRateCost
self.setup_mpc()
self.solution_invalid_cnt = 0
self.params = Params()
# Lane change
self.lane_change_enabled = self.params.get('LaneChangeEnabled') == b'1'
self.lane_change_auto_delay = self.params.get_OpkrAutoLanechangedelay()
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
self.lane_change_run_timer = 0.0
self.lane_change_wait_timer = 0.0
self.lane_change_ll_prob = 1.0
self.prev_one_blinker = False
def limit_ctrl(self, value, limit, offset):
p_limit = offset + limit
m_limit = offset - limit
if value > p_limit:
value = p_limit
elif value < m_limit:
value = m_limit
return value
def setup_mpc(self):
self.libmpc = libmpc_py.libmpc
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
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].delta = 0.0
self.angle_steers_des = 0.0
self.angle_steers_des_mpc = 0.0
self.angle_steers_des_prev = 0.0
self.angle_steers_des_time = 0.0
def update(self, sm, pm, CP, VM):
v_ego = sm['carState'].vEgo
v_ego_kph = v_ego * 3.61
angle_steers = sm['carState'].steeringAngle
steeringTorque = sm['carState'].steeringTorque
steeringPressed = sm['carState'].steeringPressed
active = sm['controlsState'].active
angle_offset = sm['liveParameters'].angleOffset
# Run MPC
self.angle_steers_des_prev = self.angle_steers_des_mpc
# Update vehicle model
x = max(sm['liveParameters'].stiffnessFactor, 0.1)
sr = max(sm['liveParameters'].steerRatio, 0.1)
VM.update_params(x, sr)
curvature_factor = VM.curvature_factor(v_ego)
self.LP.parse_model(sm['model'])
# Lane change logic
one_blinker = sm['carState'].leftBlinker != sm['carState'].rightBlinker
below_lane_change_speed = v_ego < LANE_CHANGE_SPEED_MIN
if sm['carState'].leftBlinker:
self.lane_change_direction = LaneChangeDirection.left
elif sm['carState'].rightBlinker:
self.lane_change_direction = LaneChangeDirection.right
if (not active) or (self.lane_change_run_timer >
LANE_CHANGE_TIME_MAX) or (not one_blinker) or (
not self.lane_change_enabled):
self.lane_change_state = LaneChangeState.off
self.lane_change_direction = LaneChangeDirection.none
else:
torque_applied = sm['carState'].steeringPressed and \
((sm['carState'].steeringTorque > 0 and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].steeringTorque < 0 and self.lane_change_direction == LaneChangeDirection.right))
blindspot_detected = (
(sm['carState'].leftBlindspot
and self.lane_change_direction == LaneChangeDirection.left) or
(sm['carState'].rightBlindspot
and self.lane_change_direction == LaneChangeDirection.right))
lane_change_prob = self.LP.l_lane_change_prob + self.LP.r_lane_change_prob
# State transitions
# off
if self.lane_change_state == LaneChangeState.off and one_blinker and not self.prev_one_blinker and not below_lane_change_speed:
self.lane_change_state = LaneChangeState.preLaneChange
self.lane_change_ll_prob = 1.0
self.lane_change_wait_timer = 0
# pre
elif self.lane_change_state == LaneChangeState.preLaneChange:
self.lane_change_wait_timer += DT_MDL
if not one_blinker or below_lane_change_speed:
self.lane_change_state = LaneChangeState.off
elif not blindspot_detected and (
torque_applied or (self.lane_change_auto_delay
and self.lane_change_wait_timer >
self.lane_change_auto_delay)):
self.lane_change_state = LaneChangeState.laneChangeStarting
# starting
elif self.lane_change_state == LaneChangeState.laneChangeStarting:
# fade out over .5s
# ATOM logic add
xp = [40, 60, 70, 80]
fp2 = [0.5, 1, 1.5, 2]
lane_time = interp(v_ego_kph, xp, fp2)
# <==
self.lane_change_ll_prob = max(
self.lane_change_ll_prob - lane_time * DT_MDL, 0.0)
# 98% certainty
if lane_change_prob < 0.02 and self.lane_change_ll_prob < 0.01:
self.lane_change_state = LaneChangeState.laneChangeFinishing
# finishing
elif self.lane_change_state == LaneChangeState.laneChangeFinishing:
# fade in laneline over 1s
self.lane_change_ll_prob = min(
self.lane_change_ll_prob + DT_MDL, 1.0)
if one_blinker and self.lane_change_ll_prob > 0.99:
self.lane_change_state = LaneChangeState.laneChangeDone
# done
elif self.lane_change_state == LaneChangeState.laneChangeDone:
if not one_blinker:
self.lane_change_state = LaneChangeState.off
if self.lane_change_state in [
LaneChangeState.off, LaneChangeState.preLaneChange
]:
self.lane_change_run_timer = 0.0
else:
self.lane_change_run_timer += DT_MDL
self.prev_one_blinker = one_blinker
desire = DESIRES[self.lane_change_direction][self.lane_change_state]
# Turn off lanes during lane change
if desire == log.PathPlan.Desire.laneChangeRight or desire == log.PathPlan.Desire.laneChangeLeft:
self.LP.l_prob *= self.lane_change_ll_prob
self.LP.r_prob *= self.lane_change_ll_prob
self.LP.update_d_poly(v_ego)
# account for actuation delay
self.cur_state = calc_states_after_delay(self.cur_state, v_ego,
angle_steers - angle_offset,
curvature_factor, VM.sR,
CP.steerActuatorDelay)
v_ego_mpc = max(v_ego, 5.0) # avoid mpc roughness due to low speed
self.libmpc.run_mpc(self.cur_state, self.mpc_solution,
list(self.LP.l_poly), list(self.LP.r_poly),
list(self.LP.d_poly), self.LP.l_prob,
self.LP.r_prob, curvature_factor, v_ego_mpc,
self.LP.lane_width)
# reset to current steer angle if not active or overriding
if active:
delta_desired = self.mpc_solution[0].delta[1]
rate_desired = math.degrees(self.mpc_solution[0].rate[0] * VM.sR)
else:
delta_desired = math.radians(angle_steers - angle_offset) / VM.sR
rate_desired = 0.0
self.cur_state[0].delta = delta_desired
self.angle_steers_des_mpc = float(
math.degrees(delta_desired * VM.sR) + angle_offset)
org_angle_steers_des = self.angle_steers_des_mpc
# atom
if steeringPressed:
delta_steer = self.angle_steers_des_mpc - angle_steers
xp = [-450, 0, 450]
fp2 = [5, 0, 5]
limit_steers = interp(steeringTorque, xp, fp2)
if steeringTorque < 0: # right
if delta_steer > 0:
self.angle_steers_des_mpc = self.limit_ctrl(
org_angle_steers_des, limit_steers, angle_steers)
elif steeringTorque > 0: # left
if delta_steer < 0:
self.angle_steers_des_mpc = self.limit_ctrl(
org_angle_steers_des, limit_steers, angle_steers)
elif v_ego_kph < 30: # 30
xp = [5, 15, 30]
fp2 = [1, 3, 5]
limit_steers = interp(v_ego_kph, xp, fp2)
self.angle_steers_des_mpc = self.limit_ctrl(
org_angle_steers_des, limit_steers, angle_steers)
# 가변 sR rate_cost
# self.atom_sr_boost_bp = [ 1.5, 5.0, 10.0, 15.0, 20.0, 30.0, 50.0, 60.0, 100.0, 300.0]
# self.sR_Cost = [0.50, 0.41, 0.34, 0.28, 0.24, 0.18, 0.12, 0.10, 0.05, 0.01]
# self.steer_rate_cost = interp(abs(angle_steers), self.atom_sr_boost_bp, self.sR_Cost)
# # 설정값 분석을 위한 임시 코드
# self.scale = CP.lateralTuning.lqr.scale
# self.ki = CP.lateralTuning.lqr.ki
# self.dc_gain = CP.lateralTuning.lqr.dcGain
# self.steerRatio = VM.sR
# self.laneWidth = float(self.LP.lane_width)
# self.dPoly = [float(x) for x in self.LP.d_poly]
# self.lPoly = [float(x) for x in self.LP.l_poly]
# self.lProb = float(self.LP.l_prob)
# self.rPoly = [float(x) for x in self.LP.r_poly]
# self.rProb = float(self.LP.r_prob)
# str2 = '/{} /{} /{} /{} /{} /{} /{} /{} /{} /{} /{} /{} /{} /{} /{}'.format(
# v_ego_kph, angle_steers, self.angle_steers_des_mpc, angle_offset, steeringTorque, self.scale, self.ki, self.dc_gain, self.steerRatio, self.laneWidth, self.dPoly, self.lPoly, self.lProb, self.rPoly, self.rProb )
# self.PathPlan.add( str2 )
# ###############################
# Check for infeasable MPC solution
mpc_nans = any(math.isnan(x) for x in self.mpc_solution[0].delta)
t = sec_since_boot()
if mpc_nans:
self.libmpc.init(MPC_COST_LAT.PATH, MPC_COST_LAT.LANE,
MPC_COST_LAT.HEADING, self.steer_rate_cost)
self.cur_state[0].delta = math.radians(angle_steers -
angle_offset) / VM.sR
if t > self.last_cloudlog_t + 5.0:
self.last_cloudlog_t = t
cloudlog.warning("Lateral mpc - nan: True")
if self.mpc_solution[
0].cost > 20000. or mpc_nans: # TODO: find a better way to detect when MPC did not converge
self.solution_invalid_cnt += 1
else:
self.solution_invalid_cnt = 0
plan_solution_valid = self.solution_invalid_cnt < 3
plan_send = messaging.new_message('pathPlan')
plan_send.valid = sm.all_alive_and_valid(service_list=[
'carState', 'controlsState', 'liveParameters', 'model'
])
plan_send.pathPlan.laneWidth = float(self.LP.lane_width)
plan_send.pathPlan.dPoly = [float(x) for x in self.LP.d_poly]
plan_send.pathPlan.lPoly = [float(x) for x in self.LP.l_poly]
plan_send.pathPlan.lProb = float(self.LP.l_prob)
plan_send.pathPlan.rPoly = [float(x) for x in self.LP.r_poly]
plan_send.pathPlan.rProb = float(self.LP.r_prob)
plan_send.pathPlan.angleSteers = float(self.angle_steers_des_mpc)
plan_send.pathPlan.rateSteers = float(rate_desired)
plan_send.pathPlan.angleOffset = float(
sm['liveParameters'].angleOffsetAverage)
plan_send.pathPlan.mpcSolutionValid = bool(plan_solution_valid)
plan_send.pathPlan.paramsValid = bool(sm['liveParameters'].valid)
plan_send.pathPlan.desire = desire
plan_send.pathPlan.laneChangeState = self.lane_change_state
plan_send.pathPlan.laneChangeDirection = self.lane_change_direction
pm.send('pathPlan', plan_send)
if LOG_MPC:
dat = messaging.new_message('liveMpc')
dat.liveMpc.x = list(self.mpc_solution[0].x)
dat.liveMpc.y = list(self.mpc_solution[0].y)
dat.liveMpc.psi = list(self.mpc_solution[0].psi)
dat.liveMpc.delta = list(self.mpc_solution[0].delta)
dat.liveMpc.cost = self.mpc_solution[0].cost
pm.send('liveMpc', dat)
