IQ.Pilot Release Commit @ f2a861c
This commit is contained in:
IQ.Lvbs CI [bot]
507
iqpilot/selfdrive/controls/lib/longitudinal_planner.py
Normal file → Executable file
507
iqpilot/selfdrive/controls/lib/longitudinal_planner.py
Normal file → Executable file
@@ -1,256 +1,297 @@
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"""
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Copyright © IQ.Lvbs, apart of Project Teal Lvbs, All Rights Reserved, licensed under https://konn3kt.com/tos
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"""
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from datetime import datetime
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#!/usr/bin/env python3
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import math
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import numpy as np
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from cereal import messaging, custom
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from iqdbc.car import structs
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from openpilot.common.constants import CV
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from openpilot.common.realtime import DT_MDL
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from openpilot.selfdrive.car.cruise import V_CRUISE_MAX
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from openpilot.iqpilot.selfdrive.controls.lib.custom_stop_distance import CustomStopDistance
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from openpilot.iqpilot.selfdrive.controls.lib.iq_dynamic.engine import IQDynamicController
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from openpilot.iqpilot.selfdrive.controls.lib.iq_dynamic.imahelper import IQConstants
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from openpilot.iqpilot.selfdrive.controls.lib.helpers.e2e_alerts import EndToEndAlertEngine
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from openpilot.iqpilot.selfdrive.controls.lib.slc_vcruise import SLCVCruise
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from openpilot.iqpilot.selfdrive.controls.lib.speed_limit_controller import LIMIT_ADAPT_ACC
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from openpilot.iqpilot.selfdrive.selfdrived.events import IQEvents
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from openpilot.iqpilot.selfdrive.iqmodeld.models.helpers import get_active_bundle
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import iqpilot.cereal.messaging as messaging
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from iqdbc.car.interfaces import ACCEL_MIN, ACCEL_MAX
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from iqpilot.common.constants import CV
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from iqpilot.common.filter_simple import FirstOrderFilter
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from iqpilot.common.params import Params, UnknownKeyName
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from iqpilot.common.realtime import DT_MDL
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from iqpilot.selfdrive.iqmodeld.config import ModelConstants
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from iqpilot.selfdrive.controls.lib.longcontrol import LongCtrlState
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from iqpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import LongitudinalMpc, LongitudinalPlanSource
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from iqpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import T_IDXS as T_IDXS_MPC
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from iqpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N, DEFAULT_STOPPING_SPEED, get_accel_from_plan
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from iqpilot.selfdrive.car.cruise import V_CRUISE_MAX, V_CRUISE_UNSET
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from iqpilot.common.swaglog import cloudlog
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from iqpilot.common.issue_debug import log_issue_limited
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IQDynamicState = custom.IQPlan.IQDynamicControl.IQDynamicControlState
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LongitudinalPlanSource = custom.IQPlan.LongitudinalPlanSource
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SpeedLimitAssistState = custom.IQPlan.SpeedLimit.AssistState
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SpeedLimitSource = custom.IQPlan.SpeedLimit.Source
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NavProvider = custom.IQNavState.LongitudinalProvider
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NavLongitudinalState = custom.IQNavState.LongitudinalState
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from iqpilot.selfdrive.controls.lib.iq_longitudinal_planner import LongitudinalPlannerIQ
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class LongitudinalPlannerIQ:
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def __init__(self, CP: structs.CarParams, CP_IQ: structs.IQCarParams, mpc):
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self.events_iq = IQEvents()
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self.iq_dynamic = IQDynamicController(CP, mpc)
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self.custom_stop_distance = CustomStopDistance()
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self.slimit = SLCVCruise()
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self.generation = int(model_bundle.generation) if (model_bundle := get_active_bundle()) else None
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self.source = LongitudinalPlanSource.cruise
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self.e2e_alerts = EndToEndAlertEngine()
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self.output_v_target = 0.
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self.output_a_target = 0.
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self.speed_limit_last = 0.
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self.speed_limit_final_last = 0.
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self.speed_limit_source = SpeedLimitSource.none
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self.nav_engaged = False
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self.nav_provider = NavProvider.none
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self.nav_state = NavLongitudinalState.disabled
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self.nav_speed_target = 0.
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self.nav_accel_target = 0.
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self.nav_valid = False
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self.force_stop_timer = 0.0
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self.forcing_stop = False
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self.override_force_stop = False
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self.override_force_stop_timer = 0.0
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self.tracked_model_length = 0.0
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A_CRUISE_MAX_VALS = [2.0, 1.6, 0.8, 0.6]
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A_CRUISE_MAX_BP = [0., 10.0, 25., 40.]
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A_CRUISE_MIN = -1.2
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J_CRUISE = 1.0
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CONTROL_N_T_IDX = ModelConstants.T_IDXS[:CONTROL_N]
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ALLOW_THROTTLE_THRESHOLD = 0.4
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MIN_ALLOW_THROTTLE_SPEED = 2.5
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def is_e2e(self, sm: messaging.SubMaster) -> bool:
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experimental_mode = sm['selfdriveState'].experimentalMode
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if not self.iq_dynamic.active():
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return experimental_mode
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LAUNCH_DISARM_SPEED = 2.0
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LAUNCH_COMMIT_T = 3.5
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LAUNCH_MOVING_SPEED = 1.2
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LAUNCH_MAX_ACCEL = 1.5
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return experimental_mode and self.iq_dynamic.mode() == "blended"
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E2E_CRUISE_CONVERGENCE_TAU = 15.0
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E2E_CRUISE_ACCEL_MAX = 0.5
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E2E_MODEL_SPEED_HORIZON = 5.0
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E2E_ACCEL_INTENT_BP = [-0.05, 0.05]
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E2E_MODEL_SPEED_INTENT_BP = [-0.5, 0.0]
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def update_targets(self, sm: messaging.SubMaster, v_ego: float, a_ego: float, v_cruise: float) -> tuple[float, float]:
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CS = sm['carState']
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v_cruise_cluster_kph = min(CS.vCruiseCluster, V_CRUISE_MAX)
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v_cruise_cluster = v_cruise_cluster_kph * CV.KPH_TO_MS
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# SLC should apply whenever IQ.Pilot is engaged, even on stock-longitudinal cars
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# where carControl.longActive stays false.
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slc_apply_enabled = bool(getattr(sm['selfdriveState'], "enabled", False))
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# Lookup table for turns
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_A_TOTAL_MAX_V = [1.7, 3.2]
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_A_TOTAL_MAX_BP = [20., 40.]
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nav_state = sm['iqNavState']
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self.nav_engaged = bool(getattr(nav_state, "longitudinalEngaged", False))
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self.nav_provider = getattr(nav_state, "longitudinalProvider", NavProvider.none)
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self.nav_state = getattr(nav_state, "longitudinalState", NavLongitudinalState.disabled)
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self.nav_speed_target = float(getattr(nav_state, "speedTarget", 0.0))
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self.nav_accel_target = float(getattr(nav_state, "accelTarget", 0.0))
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self.nav_valid = bool(getattr(nav_state, "valid", False) and self.nav_engaged)
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def get_max_accel(v_ego):
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return np.interp(v_ego, A_CRUISE_MAX_BP, A_CRUISE_MAX_VALS)
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# IQ.Pilot custom Speed Limit Controller
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now = datetime.now()
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if hasattr(sm, "alive"):
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time_validated = sm.alive.get('clocks', False) and getattr(sm['clocks'], 'timeValid', False)
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def get_coast_accel(pitch):
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return np.sin(pitch) * -5.65 - 0.3 # fitted from data using xx/projects/allow_throttle/compute_coast_accel.py
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def get_lead_distance(radarState):
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if radarState.leadOne.status and (not radarState.leadTwo.status or radarState.leadOne.dRel < radarState.leadTwo.dRel):
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return radarState.leadOne.dRel
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if radarState.leadTwo.status:
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return radarState.leadTwo.dRel
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return 0
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def get_cruise_accel(e2e, v_cruise, v_ego, a_cruise_prev, angle_steers, CP, dt, accel_coast, allow_throttle):
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max_accel = ACCEL_MAX if e2e else get_max_accel(v_ego)
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if not e2e:
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a_total_max = np.interp(v_ego, _A_TOTAL_MAX_BP, _A_TOTAL_MAX_V)
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a_y = v_ego ** 2 * angle_steers * CV.DEG_TO_RAD / (CP.steerRatio * CP.wheelbase)
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a_x_allowed = math.sqrt(max(a_total_max ** 2 - a_y ** 2, 0.))
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max_accel = min(max_accel, a_x_allowed)
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if not allow_throttle:
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clipped_accel_coast = max(accel_coast, ACCEL_MIN)
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coast_limit = np.interp(v_ego, [MIN_ALLOW_THROTTLE_SPEED, MIN_ALLOW_THROTTLE_SPEED*2], [max_accel, clipped_accel_coast])
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max_accel = min(max_accel, coast_limit)
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target_accel = np.clip(v_cruise - v_ego, A_CRUISE_MIN, max_accel)
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target_accel = float(np.clip(target_accel, a_cruise_prev - J_CRUISE * dt, a_cruise_prev + J_CRUISE * dt))
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cruise_should_stop = v_cruise == 0.0
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return target_accel, cruise_should_stop
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def get_e2e_accel(v_ego, v_cruise, model_v, a_target, should_stop):
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if should_stop or v_cruise <= v_ego or len(model_v) != len(T_IDXS_MPC):
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return a_target
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convergence_accel = min((v_cruise - v_ego) / E2E_CRUISE_CONVERGENCE_TAU, E2E_CRUISE_ACCEL_MAX)
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if convergence_accel <= a_target:
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return a_target
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# Only help the model converge to cruise when both its immediate action and
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# velocity trajectory show no active deceleration intent. The lead MPC and
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# cruise candidates remain hard upper bounds on the final acceleration.
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accel_intent = np.interp(a_target, E2E_ACCEL_INTENT_BP, [0.0, 1.0])
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model_speed = np.interp(E2E_MODEL_SPEED_HORIZON, T_IDXS_MPC, model_v)
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speed_intent = np.interp(model_speed - v_ego, E2E_MODEL_SPEED_INTENT_BP, [0.0, 1.0])
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return float(np.interp(min(accel_intent, speed_intent), [0.0, 1.0], [a_target, convergence_accel]))
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def get_accel_candidates(e2e, has_lead, mpc_candidate, cruise_candidate, e2e_candidate):
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candidates = []
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# With no lead, the MPC follows a synthetic fast lead. It remains the ACC
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# policy, but must not limit the model policy in full E2E.
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if not e2e or has_lead:
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candidates.append(mpc_candidate)
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candidates.append(cruise_candidate)
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if e2e:
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candidates.append(e2e_candidate)
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return candidates
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class LongitudinalPlanner(LongitudinalPlannerIQ):
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def __init__(self, CP, CP_IQ, init_v=0.0, init_a=0.0, dt=DT_MDL):
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self.CP = CP
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self.stopping_speed = CP_IQ.longitudinalStoppingSpeedOverride or DEFAULT_STOPPING_SPEED
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self.mpc = LongitudinalMpc(dt=dt)
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LongitudinalPlannerIQ.__init__(self, self.CP, CP_IQ, self.mpc)
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self.fcw = False
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self.dt = dt
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self.allow_throttle = True
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self.a_desired = init_a
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self.v_desired_filter = FirstOrderFilter(init_v, 2.0, self.dt)
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self.a_cruise = init_a
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self.output_a_target = 0.0
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self.output_should_stop = False
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self.launch_armed = False
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try:
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self.exp_speed_conv = Params().get_bool("expSpeedConv")
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except UnknownKeyName:
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self.exp_speed_conv = False
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self.v_desired_trajectory = np.zeros(CONTROL_N)
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self.a_desired_trajectory = np.zeros(CONTROL_N)
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self.j_desired_trajectory = np.zeros(CONTROL_N)
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@staticmethod
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def parse_model(model_msg):
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if (len(model_msg.position.x) == ModelConstants.IDX_N and
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len(model_msg.velocity.x) == ModelConstants.IDX_N and
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len(model_msg.acceleration.x) == ModelConstants.IDX_N):
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x = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.position.x)
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v = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.velocity.x)
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a = np.interp(T_IDXS_MPC, ModelConstants.T_IDXS, model_msg.acceleration.x)
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j = np.zeros(len(T_IDXS_MPC))
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else:
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clocks = sm.get('clocks', None) if isinstance(sm, dict) else None
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time_validated = bool(getattr(clocks, 'timeValid', False))
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slc_v_cruise = self.slimit.update(slc_apply_enabled, now, time_validated, v_cruise, v_ego, sm)
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self.iq_dynamic.set_slc_experimental_mode(self.slimit.slc_experimental_mode)
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self.iq_dynamic.update(sm)
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# Prefer confirmed controller output for UI/planner rendering.
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# Fall back to active (policy-resolved) target/source when confirmed is unavailable.
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display_speed_limit = self.slimit.slc_target if self.slimit.slc_target > 0 else self.slimit.slc_active_target
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display_source = self.slimit.slc_source if self.slimit.slc_source != "None" else self.slimit.slc_active_source
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if display_speed_limit > 0:
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self.speed_limit_last = display_speed_limit
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self.speed_limit_final_last = display_speed_limit + self.slimit.slc_offset
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elif display_source == "None":
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self.speed_limit_last = 0.0
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self.speed_limit_final_last = 0.0
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# Respect user-defined max cruise speed when applying SLC.
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if v_cruise_cluster > 0 and self.speed_limit_final_last > 0:
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self.speed_limit_final_last = min(self.speed_limit_final_last, v_cruise_cluster)
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source_map = {
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"Dashboard": SpeedLimitSource.car,
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"Map Data": SpeedLimitSource.map,
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"Mapbox": SpeedLimitSource.map,
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"None": SpeedLimitSource.none,
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}
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self.speed_limit_source = source_map.get(display_source, SpeedLimitSource.none)
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targets = {
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LongitudinalPlanSource.cruise: (v_cruise, a_ego),
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LongitudinalPlanSource.speedLimitAssist: (slc_v_cruise, a_ego),
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}
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if self.nav_valid:
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targets[LongitudinalPlanSource.nav] = (self.nav_speed_target, self.nav_accel_target)
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self.source = min(targets, key=lambda k: targets[k][0])
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self.output_v_target, self.output_a_target = targets[self.source]
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self.output_v_target = self._apply_force_stop(self.output_v_target, v_ego, sm, slc_apply_enabled)
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# envelope shaping only in Assist mode: info/warn must never change the plan
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self._envelope_enabled = (slc_apply_enabled and bool(getattr(self.slimit, "controller_enabled", False))
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and bool(getattr(self.slimit, "mode_assist", False)))
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return self.output_v_target, self.output_a_target
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def cruise_envelope(self, v_target: float, v_ego: float, t_idxs) -> np.ndarray:
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"""Per-timestep cruise speed over the MPC horizon: the scalar target, shaped down
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ahead of an upcoming lower speed limit so the solver decelerates before the sign
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instead of at it."""
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env = np.full(len(t_idxs), max(float(v_target), 0.0))
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if not getattr(self, "_envelope_enabled", False):
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return env
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slc = getattr(self.slimit, "slc", None)
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next_limit = float(getattr(slc, "next_speed_limit", 0.0) or 0.0)
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next_dist = float(getattr(slc, "next_speed_distance", 0.0) or 0.0)
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if next_limit <= 0.0 or next_dist <= 0.0:
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return env
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next_target = max(next_limit + float(getattr(self.slimit, "slc_offset", 0.0) or 0.0), 0.0)
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if next_target >= env[0]:
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return env
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travel = np.maximum(v_ego, 1.0) * np.asarray(t_idxs)
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v_allowed = np.sqrt(np.maximum(next_target ** 2 + 2.0 * abs(LIMIT_ADAPT_ACC) * (next_dist - travel), next_target ** 2))
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return np.minimum(env, v_allowed)
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def update(self, sm: messaging.SubMaster) -> None:
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self.events_iq.clear()
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for event_name in getattr(self.slimit, 'pending_events', []):
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self.events_iq.add(event_name)
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self.custom_stop_distance.update()
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self.e2e_alerts.update(sm, self.events_iq)
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if bool(getattr(sm["iqCarState"], "alcOverrideAlert", False)):
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self.events_iq.add(custom.IQOnroadEvent.EventName.steeringOverrideReengageAlc)
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def apply_e2e_stop_distance(self, sm: messaging.SubMaster, v_ego: float, a_target: float, should_stop: bool) -> tuple[float, bool]:
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if not self.is_e2e(sm):
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return a_target, should_stop
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return self.custom_stop_distance.adjust_e2e_stop(a_target, should_stop, v_ego, sm['modelV2'])
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def _apply_force_stop(self, v_target: float, v_ego: float, sm: messaging.SubMaster, apply_enabled: bool) -> float:
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force_stop = self.iq_dynamic.force_stop_requested() and apply_enabled and self.override_force_stop_timer <= 0.0
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self.force_stop_timer = self.force_stop_timer + DT_MDL if force_stop else 0.0
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force_stop_enabled = self.force_stop_timer >= 1.0
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force_stop_ramp_time = max(float(getattr(self.iq_dynamic, "model_stop_time", IQConstants.FORCE_STOP_PLANNER_TIME)), DT_MDL)
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accel_pressed = bool(getattr(sm["iqCarState"], "accelPressed", False))
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self.override_force_stop |= sm["carState"].gasPressed or accel_pressed
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self.override_force_stop &= force_stop_enabled
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if self.override_force_stop:
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self.override_force_stop_timer = 10.0
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elif self.override_force_stop_timer > 0.0:
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self.override_force_stop_timer = max(0.0, self.override_force_stop_timer - DT_MDL)
|
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x = np.zeros(len(T_IDXS_MPC))
|
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v = np.zeros(len(T_IDXS_MPC))
|
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a = np.zeros(len(T_IDXS_MPC))
|
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j = np.zeros(len(T_IDXS_MPC))
|
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if len(model_msg.meta.disengagePredictions.gasPressProbs) > 1:
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throttle_prob = model_msg.meta.disengagePredictions.gasPressProbs[1]
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else:
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self.override_force_stop = False
|
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throttle_prob = 1.0
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return x, v, a, j, throttle_prob
|
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|
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if force_stop_enabled and not self.override_force_stop:
|
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self.forcing_stop = True
|
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self.tracked_model_length = max(self.tracked_model_length - (v_ego * DT_MDL), 0.0)
|
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if sm["carState"].standstill:
|
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return 0.0
|
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return min(self.tracked_model_length / force_stop_ramp_time, v_target)
|
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def update(self, sm):
|
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LongitudinalPlannerIQ.update(self, sm)
|
||||
|
||||
self.forcing_stop = False
|
||||
self.tracked_model_length = max(
|
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float(getattr(self.iq_dynamic, "model_length", 0.0)),
|
||||
float(getattr(self.iq_dynamic, "minimum_force_stop_length", 0.0)),
|
||||
0.0,
|
||||
if len(sm['carControl'].orientationNED) == 3:
|
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accel_coast = get_coast_accel(sm['carControl'].orientationNED[1])
|
||||
else:
|
||||
accel_coast = ACCEL_MAX
|
||||
|
||||
v_ego = sm['carState'].vEgo
|
||||
v_cruise_kph = min(sm['carState'].vCruise, V_CRUISE_MAX)
|
||||
v_cruise = v_cruise_kph * CV.KPH_TO_MS
|
||||
if sm['controlsState'].forceDecel:
|
||||
v_cruise = 0.0
|
||||
|
||||
long_control_off = sm['controlsState'].longControlState == LongCtrlState.off
|
||||
|
||||
# Reset current state when not engaged, or user is controlling the speed
|
||||
reset_state = long_control_off if self.CP.openpilotLongitudinalControl else not sm['selfdriveState'].enabled
|
||||
# PCM cruise speed may be updated a few cycles later, check if initialized
|
||||
v_cruise_initialized = sm['carState'].vCruise != V_CRUISE_UNSET
|
||||
reset_state = reset_state or not v_cruise_initialized
|
||||
steer_angle_without_offset = sm['carState'].steeringAngleDeg - sm['vehicleParameters'].angleOffsetDeg
|
||||
|
||||
if reset_state:
|
||||
self.v_desired_filter.x = v_ego
|
||||
self.a_desired = np.clip(sm['carState'].aEgo, ACCEL_MIN, ACCEL_MAX)
|
||||
self.a_cruise = self.a_desired
|
||||
|
||||
# Prevent divergence, smooth in current v_ego
|
||||
self.v_desired_filter.x = max(0.0, self.v_desired_filter.update(v_ego))
|
||||
_, model_v, model_a, _, throttle_prob = self.parse_model(sm['modelV2'])
|
||||
# Don't clip at low speeds since throttle_prob doesn't account for creep
|
||||
self.allow_throttle = throttle_prob > ALLOW_THROTTLE_THRESHOLD or v_ego <= MIN_ALLOW_THROTTLE_SPEED
|
||||
|
||||
# Get new v_cruise from Smart Cruise Control and Speed Limit Assist
|
||||
v_cruise = LongitudinalPlannerIQ.update_targets(self, sm, self.v_desired_filter.x, v_cruise)
|
||||
|
||||
if sm['controlsState'].forceDecel:
|
||||
v_cruise = 0.0
|
||||
|
||||
personality = sm['selfdriveState'].personality
|
||||
self.mpc.set_weights(personality=personality)
|
||||
self.mpc.set_cur_state(self.v_desired_filter.x, self.a_desired)
|
||||
self.mpc.update(sm['modelV2'], sm['radarState'], personality=personality)
|
||||
|
||||
self.v_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.v_solution)
|
||||
self.a_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC, self.mpc.a_solution)
|
||||
self.j_desired_trajectory = np.interp(CONTROL_N_T_IDX, T_IDXS_MPC[:-1], self.mpc.j_solution)
|
||||
|
||||
# TODO counter is only needed because radar is glitchy, remove once radar is gone
|
||||
self.fcw = self.mpc.crash_cnt > 2 and not sm['carState'].standstill
|
||||
if self.fcw:
|
||||
cloudlog.info("FCW triggered")
|
||||
|
||||
# Save starting point for next iteration
|
||||
a_prev = self.a_desired
|
||||
|
||||
action_t = self.CP.longitudinalActuatorDelay + DT_MDL
|
||||
output_a_target_mpc, output_should_stop_mpc = get_accel_from_plan(self.v_desired_trajectory, self.a_desired_trajectory, CONTROL_N_T_IDX,
|
||||
action_t=action_t, stopping_speed=self.stopping_speed)
|
||||
|
||||
output_a_target_e2e = sm['modelV2'].action.desiredAcceleration
|
||||
output_should_stop_e2e = sm['modelV2'].action.shouldStop
|
||||
output_a_target_e2e, output_should_stop_e2e = self.apply_e2e_stop_distance(sm, v_ego, output_a_target_e2e, output_should_stop_e2e)
|
||||
if self.is_e2e(sm) and self.exp_speed_conv and not self.mpc.status:
|
||||
output_a_target_e2e = get_e2e_accel(v_ego, v_cruise, model_v, output_a_target_e2e, output_should_stop_e2e)
|
||||
|
||||
if sm['carState'].standstill:
|
||||
self.launch_armed = True
|
||||
elif v_ego > LAUNCH_DISARM_SPEED:
|
||||
self.launch_armed = False
|
||||
if (self.launch_armed and self.is_e2e(sm) and not output_should_stop_e2e and
|
||||
np.interp(LAUNCH_COMMIT_T, T_IDXS_MPC, model_v) > LAUNCH_DISARM_SPEED):
|
||||
t_cut = min(float(T_IDXS_MPC[np.argmax(model_v > LAUNCH_MOVING_SPEED)]), LAUNCH_COMMIT_T)
|
||||
t_shifted = T_IDXS_MPC + t_cut
|
||||
v_shifted = np.interp(t_shifted, T_IDXS_MPC, model_v)
|
||||
a_shifted = np.interp(t_shifted, T_IDXS_MPC, model_a)
|
||||
a_launch = get_accel_from_plan(v_shifted, a_shifted, T_IDXS_MPC, action_t=action_t)[0]
|
||||
a_launch_max = np.interp(v_ego, [LAUNCH_MOVING_SPEED, LAUNCH_DISARM_SPEED], [LAUNCH_MAX_ACCEL, 0.])
|
||||
output_a_target_e2e = max(output_a_target_e2e, min(a_launch, a_launch_max))
|
||||
|
||||
e2e = self.is_e2e(sm)
|
||||
self.a_cruise, cruise_should_stop = get_cruise_accel(e2e, v_cruise, v_ego, self.a_cruise,
|
||||
steer_angle_without_offset, self.CP, self.dt,
|
||||
accel_coast, self.allow_throttle)
|
||||
|
||||
candidates = get_accel_candidates(
|
||||
e2e,
|
||||
self.mpc.status,
|
||||
(output_a_target_mpc, self.mpc.source, output_should_stop_mpc),
|
||||
(self.a_cruise, LongitudinalPlanSource.cruise, cruise_should_stop),
|
||||
(output_a_target_e2e, LongitudinalPlanSource.e2e, output_should_stop_e2e),
|
||||
)
|
||||
return v_target
|
||||
|
||||
def publish_longitudinal_plan_iq(self, sm: messaging.SubMaster, pm: messaging.PubMaster) -> None:
|
||||
def fill_plan(plan_msg) -> None:
|
||||
plan_msg.longitudinalPlanSource = self.source
|
||||
plan_msg.vTarget = float(self.output_v_target)
|
||||
plan_msg.aTarget = float(self.output_a_target)
|
||||
plan_msg.events = self.events_iq.to_msg()
|
||||
output_a_target, self.mpc.source, _ = min(candidates, key=lambda c: c[0])
|
||||
self.output_should_stop = any(should_stop for _, _, should_stop in candidates)
|
||||
|
||||
# IQ.Dynamic control state
|
||||
iq_dynamic = plan_msg.iqDynamic
|
||||
iq_dynamic.state = IQDynamicState.blended if self.iq_dynamic.mode() == 'blended' else IQDynamicState.acc
|
||||
iq_dynamic.enabled = self.iq_dynamic.enabled()
|
||||
iq_dynamic.active = self.iq_dynamic.active()
|
||||
self.output_should_stop = self.output_should_stop or self.forcing_stop
|
||||
self.output_a_target = np.clip(output_a_target, ACCEL_MIN, ACCEL_MAX)
|
||||
|
||||
nav_summary = plan_msg.iqNavState.nav
|
||||
nav_summary.engaged = self.nav_engaged
|
||||
nav_summary.provider = self.nav_provider
|
||||
nav_summary.state = self.nav_state
|
||||
nav_summary.speedTarget = float(self.nav_speed_target)
|
||||
nav_summary.accelTarget = float(self.nav_accel_target)
|
||||
nav_summary.valid = self.nav_valid
|
||||
self.a_desired = float(self.output_a_target)
|
||||
self.v_desired_filter.x = self.v_desired_filter.x + self.dt * (self.output_a_target + a_prev) / 2.0
|
||||
|
||||
# Speed Limit
|
||||
speedLimit = plan_msg.speedLimit
|
||||
resolver = speedLimit.resolver
|
||||
speed_limit = float(self.slimit.slc_target if self.slimit.slc_target > 0 else self.slimit.slc_active_target)
|
||||
speed_limit_offset = float(self.slimit.slc_offset)
|
||||
speed_limit_final = speed_limit + speed_limit_offset if speed_limit > 0 else 0.
|
||||
speed_limit_valid = speed_limit > 0.
|
||||
speed_limit_last_valid = self.speed_limit_last > 0.
|
||||
def publish(self, sm, pm):
|
||||
plan_send = messaging.new_message('longitudinalPlan')
|
||||
|
||||
resolver.speedLimit = speed_limit
|
||||
resolver.speedLimitLast = float(self.speed_limit_last)
|
||||
resolver.speedLimitFinal = float(speed_limit_final)
|
||||
resolver.speedLimitFinalLast = float(self.speed_limit_final_last)
|
||||
resolver.speedLimitValid = speed_limit_valid
|
||||
resolver.speedLimitLastValid = speed_limit_last_valid
|
||||
resolver.speedLimitOffset = speed_limit_offset
|
||||
resolver.distToSpeedLimit = 0.
|
||||
resolver.source = self.speed_limit_source
|
||||
gate_services = ['carState', 'controlsState', 'selfdriveState', 'radarState']
|
||||
plan_send.valid = sm.all_checks(service_list=gate_services)
|
||||
if not plan_send.valid:
|
||||
log_issue_limited(
|
||||
"longitudinal_plan_invalid",
|
||||
"planner",
|
||||
f"longitudinalPlan invalid alive={ {s: sm.alive[s] for s in gate_services} } "
|
||||
f"freq_ok={ {s: sm.freq_ok[s] for s in gate_services} } valid={ {s: sm.valid[s] for s in gate_services} } "
|
||||
f"subchecks=({sm.all_alive(gate_services)},{sm.all_freq_ok(gate_services)},{sm.all_valid(gate_services)}) "
|
||||
f"recheck={sm.all_checks(service_list=gate_services)}",
|
||||
interval_sec=5.0,
|
||||
)
|
||||
|
||||
assist = speedLimit.assist
|
||||
slc_assist_state = self.slimit.assist_state
|
||||
assist.enabled = bool(self.slimit.slc_target > 0 or self.slimit.slc_unconfirmed > 0)
|
||||
assist.active = self.source == LongitudinalPlanSource.speedLimitAssist and self.slimit.slc_target > 0
|
||||
if slc_assist_state is not None:
|
||||
assist.state = slc_assist_state
|
||||
elif not assist.enabled:
|
||||
assist.state = SpeedLimitAssistState.disabled
|
||||
elif self.slimit.slc_unconfirmed > 0:
|
||||
assist.state = SpeedLimitAssistState.preActive
|
||||
elif assist.active:
|
||||
assist.state = SpeedLimitAssistState.active
|
||||
else:
|
||||
assist.state = SpeedLimitAssistState.inactive
|
||||
assist.vTarget = float(self.output_v_target if assist.active else 255.)
|
||||
assist.aTarget = float(self.slimit.slc_a_target if assist.active else 0.)
|
||||
longitudinalPlan = plan_send.longitudinalPlan
|
||||
longitudinalPlan.modelMonoTime = sm.logMonoTime['modelV2']
|
||||
longitudinalPlan.processingDelay = (plan_send.logMonoTime / 1e9) - sm.logMonoTime['modelV2']
|
||||
longitudinalPlan.solverExecutionTime = self.mpc.solve_time
|
||||
|
||||
e2eAlerts = plan_msg.e2eAlerts
|
||||
e2eAlerts.pathOpen = self.e2e_alerts.path_alert
|
||||
e2eAlerts.leadPullaway = self.e2e_alerts.lead_alert
|
||||
longitudinalPlan.speeds = self.v_desired_trajectory.tolist()
|
||||
longitudinalPlan.accels = self.a_desired_trajectory.tolist()
|
||||
longitudinalPlan.jerks = self.j_desired_trajectory.tolist()
|
||||
|
||||
valid = sm.all_checks(service_list=['carState', 'controlsState'])
|
||||
longitudinalPlan.hasLead = sm['radarState'].leadOne.status
|
||||
longitudinalPlan.leadDistance = get_lead_distance(sm['radarState'])
|
||||
longitudinalPlan.longitudinalPlanSource = self.mpc.source
|
||||
longitudinalPlan.fcw = self.fcw
|
||||
|
||||
plan_iq_send = messaging.new_message('iqPlan')
|
||||
plan_iq_send.valid = valid
|
||||
fill_plan(plan_iq_send.iqPlan)
|
||||
pm.send('iqPlan', plan_iq_send)
|
||||
longitudinalPlan.leadTrajectoryX0 = self.mpc.lead_xv_0[:, 0].tolist()
|
||||
longitudinalPlan.leadTrajectoryV0 = self.mpc.lead_xv_0[:, 1].tolist()
|
||||
longitudinalPlan.leadTrajectoryX1 = self.mpc.lead_xv_1[:, 0].tolist()
|
||||
longitudinalPlan.leadTrajectoryV1 = self.mpc.lead_xv_1[:, 1].tolist()
|
||||
|
||||
longitudinalPlan.aTarget = float(self.output_a_target)
|
||||
longitudinalPlan.shouldStop = bool(self.output_should_stop)
|
||||
longitudinalPlan.allowBrake = True
|
||||
longitudinalPlan.allowThrottle = bool(self.allow_throttle)
|
||||
|
||||
pm.send('longitudinalPlan', plan_send)
|
||||
|
||||
self.publish_longitudinal_plan_iq(sm, pm)
|
||||
|
||||
Reference in New Issue
Block a user