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https://github.com/firestar5683/StarPilot.git
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firestar5683
325 lines
10 KiB
Python
325 lines
10 KiB
Python
from types import SimpleNamespace
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import numpy as np
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import pytest
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from cereal import log
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from opendbc.car.honda.interface import CarInterface
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from opendbc.car.honda.values import CAR
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from opendbc.car.gm.values import CAR as GM_CAR, GMFlags
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import openpilot.selfdrive.controls.lib.longitudinal_planner as planner_module
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from openpilot.selfdrive.controls.lib.longcontrol import LongCtrlState
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from openpilot.selfdrive.controls.lib.longitudinal_mpc_lib.long_mpc import (
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T_IDXS,
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LongitudinalMpc,
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should_trigger_planner_fcw,
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)
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from openpilot.selfdrive.controls.lib.longitudinal_planner import (
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MIN_PLAN_HORIZON,
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LongitudinalPlanner,
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PlanState,
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get_vehicle_min_accel,
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)
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from openpilot.selfdrive.controls.lib.longitudinal_vehicle_tunes import get_longitudinal_planner_tune
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from openpilot.selfdrive.modeld.constants import ModelConstants
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def make_lead(*, status=False, d_rel=200.0, v_lead=0.0, a_lead=0.0,
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radar=False, model_prob=1.0):
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lead = log.RadarState.LeadData.new_message()
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lead.status = status
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lead.dRel = d_rel
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lead.vLead = v_lead
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lead.vLeadK = v_lead
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lead.vRel = 0.0
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lead.aLeadK = a_lead
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lead.aLeadTau = 1.5
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lead.modelProb = model_prob
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lead.radar = radar
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return lead
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def make_model(v_ego, desired_accel=0.0):
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model = log.ModelDataV2.new_message()
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times = np.asarray(ModelConstants.T_IDXS)
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model.position.x = (v_ego * times).tolist()
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model.position.y = [0.0] * len(times)
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model.position.z = [0.0] * len(times)
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model.position.t = times.tolist()
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model.velocity.x = [v_ego] * len(times)
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model.velocity.y = [0.0] * len(times)
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model.velocity.z = [0.0] * len(times)
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model.velocity.t = times.tolist()
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model.acceleration.x = [0.0] * len(times)
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model.acceleration.y = [0.0] * len(times)
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model.acceleration.z = [0.0] * len(times)
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model.acceleration.t = times.tolist()
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model.meta.disengagePredictions.gasPressProbs = [1.0] * 6
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model.action.desiredAcceleration = desired_accel
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model.action.shouldStop = False
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return model
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def set_model_departure(model, accel=0.5):
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times = np.asarray(ModelConstants.T_IDXS)
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model.position.x = (2.0 * times + 0.5 * accel * times ** 2).tolist()
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model.velocity.x = (2.0 + accel * times).tolist()
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model.acceleration.x = [accel] * len(times)
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model.action.desiredAcceleration = accel
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model.action.shouldStop = False
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def set_model_stop(model, distance=5.0):
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count = len(ModelConstants.T_IDXS)
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model.position.x = np.linspace(0.0, distance, count).tolist()
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model.velocity.x = np.linspace(10.0, 0.0, count).tolist()
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model.action.desiredAcceleration = -1.0
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def make_sm(v_ego=20.0, *, v_cruise=None, model_accel=0.0, lead_one=None, lead_two=None):
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v_cruise = v_ego + 5.0 if v_cruise is None else v_cruise
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return {
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"carControl": SimpleNamespace(orientationNED=[0.0, 0.0, 0.0]),
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"carState": SimpleNamespace(
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vEgo=v_ego,
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vEgoCluster=v_ego,
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aEgo=0.0,
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vCruise=100.0,
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standstill=v_ego == 0.0,
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steeringAngleDeg=0.0,
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brakePressed=False,
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leftBlinker=False,
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rightBlinker=False,
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),
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"controlsState": SimpleNamespace(longControlState=LongCtrlState.pid, forceDecel=False),
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"liveParameters": SimpleNamespace(angleOffsetDeg=0.0),
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"modelV2": make_model(v_ego, model_accel),
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"radarState": SimpleNamespace(
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leadOne=lead_one or make_lead(),
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leadTwo=lead_two or make_lead(),
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),
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"selfdriveState": SimpleNamespace(
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enabled=True,
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personality=log.LongitudinalPersonality.standard,
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),
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"starpilotPlan": SimpleNamespace(
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vCruise=v_cruise,
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minAcceleration=-3.5,
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maxAcceleration=2.0,
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accelerationJerk=200.0,
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dangerJerk=100.0,
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speedJerk=5.0,
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dangerFactor=0.75,
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tFollow=1.45,
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disableThrottle=False,
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forcingStop=False,
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redLight=False,
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stopSignConfirmed=False,
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forcingStopLength=2.0,
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increasedStoppedDistance=0.0,
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roadCurvature=0.0,
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),
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}
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def make_toggles(*, model_first=False, delay=0.2):
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return SimpleNamespace(
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longitudinal_model_preference=model_first,
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longitudinalActuatorDelay=delay,
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vEgoStopping=0.05,
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stopAccel=-0.5,
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)
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def use_fixed_mpc(planner, accel):
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def update(*_args, **_kwargs):
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v_ego = planner.mpc.x0[1]
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planner.mpc.a_solution = np.full(len(T_IDXS), accel)
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planner.mpc.v_solution = np.maximum(v_ego + accel * T_IDXS, 0.0)
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planner.mpc.j_solution = np.zeros(len(T_IDXS) - 1)
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planner.mpc.source = "cruise"
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planner.mpc.crash_cnt = 0
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planner.mpc.update = update
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def test_set_speed_first_and_model_first_are_continuous_preferences():
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CP = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
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sm = make_sm(v_ego=20.0, v_cruise=20.0, model_accel=-0.4)
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set_speed = LongitudinalPlanner(CP, init_v=20.0)
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use_fixed_mpc(set_speed, 0.5)
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set_speed.update(sm, make_toggles(model_first=False))
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model_first = LongitudinalPlanner(CP, init_v=20.0)
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use_fixed_mpc(model_first, 0.5)
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model_first.update(sm, make_toggles(model_first=True))
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assert set_speed.model_authority == 0.0
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assert set_speed.output_a_target > 0.0
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assert model_first.model_authority >= 0.75
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assert model_first.output_a_target < 0.0
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def test_model_stop_intent_brakes_without_binary_mode_switch():
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CP = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
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planner = LongitudinalPlanner(CP, init_v=10.0)
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use_fixed_mpc(planner, 0.4)
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sm = make_sm(v_ego=10.0, model_accel=-1.0)
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set_model_stop(sm["modelV2"])
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planner.update(sm, make_toggles())
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assert planner.model_authority == 1.0
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assert planner.state == PlanState.stopping
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assert planner.output_a_target < 0.0
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assert planner.plan_source == "e2e"
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def test_force_stop_is_a_hard_stop():
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CP = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
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planner = LongitudinalPlanner(CP, init_v=0.0)
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use_fixed_mpc(planner, 0.5)
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sm = make_sm(v_ego=0.0, model_accel=0.5)
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set_model_departure(sm["modelV2"])
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sm["starpilotPlan"].forcingStop = True
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planner.update(sm, make_toggles())
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assert planner.state == PlanState.stopped
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assert planner.output_should_stop
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assert planner.output_a_target <= 0.0
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def test_second_lead_slot_can_immediately_cap_acceleration():
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CP = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
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planner = LongitudinalPlanner(CP, init_v=15.0)
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use_fixed_mpc(planner, 0.8)
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sm = make_sm(
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v_ego=15.0,
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model_accel=0.8,
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lead_one=make_lead(status=True, d_rel=150.0, v_lead=15.0, radar=True),
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lead_two=make_lead(status=True, d_rel=20.0, v_lead=0.0, radar=False),
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)
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planner.update(sm, make_toggles())
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assert planner.output_a_target < 0.0
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def test_stopped_lead_holds_until_motion_and_gap_are_both_safe():
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CP = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
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planner = LongitudinalPlanner(CP, init_v=0.0)
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use_fixed_mpc(planner, 0.5)
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lead = make_lead(status=True, d_rel=5.9, v_lead=1.0)
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sm = make_sm(v_ego=0.0, model_accel=0.5, lead_one=lead)
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set_model_departure(sm["modelV2"])
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planner.state = PlanState.stopped
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planner.update(sm, make_toggles())
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assert planner.state == PlanState.stopped
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assert planner.output_should_stop
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assert planner.output_a_target <= 0.0
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lead.dRel = 6.0
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planner.update(sm, make_toggles())
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assert planner.state == PlanState.departing
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assert planner.output_a_target >= planner.tune.launch_accel
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def test_departing_lead_that_brakes_aborts_launch_immediately():
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CP = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
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planner = LongitudinalPlanner(CP, init_v=0.0)
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use_fixed_mpc(planner, 0.5)
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lead = make_lead(status=True, d_rel=7.0, v_lead=1.0)
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sm = make_sm(v_ego=0.0, model_accel=0.5, lead_one=lead)
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set_model_departure(sm["modelV2"])
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planner.state = PlanState.stopped
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planner.update(sm, make_toggles())
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assert planner.state == PlanState.departing
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sm["carState"].vEgo = 0.4
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sm["carState"].vEgoCluster = 0.4
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sm["carState"].standstill = False
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lead.vLead = 0.1
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lead.aLeadK = -1.0
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planner.update(sm, make_toggles())
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assert planner.state == PlanState.stopping
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assert planner.output_a_target <= -0.5
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def test_output_smoothing_is_asymmetric_and_urgent_braking_bypasses_it():
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CP = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
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planner = LongitudinalPlanner(CP)
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planner.output_a_target = 0.0
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assert planner._smooth_output(1.0, urgent=False) == pytest.approx(
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planner.tune.accel_slew_rate * planner.dt
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)
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assert planner._smooth_output(-2.0, urgent=True) == -2.0
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def test_low_actuator_delay_uses_stable_evaluation_horizon(monkeypatch):
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CP = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
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planner = LongitudinalPlanner(CP, init_v=20.0)
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use_fixed_mpc(planner, 0.0)
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captured = {}
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def capture_plan(_speeds, _accels, action_t, _v_ego_stopping):
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captured["action_t"] = action_t
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return 0.0, False
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monkeypatch.setattr(planner_module, "get_accel_from_plan", capture_plan)
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planner.update(make_sm(v_ego=20.0), make_toggles(delay=0.05))
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assert captured["action_t"] == pytest.approx(MIN_PLAN_HORIZON)
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def test_mpc_tracks_both_lead_slots_independently_without_fusion():
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mpc = LongitudinalMpc()
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mpc.set_cur_state(20.0, 0.0)
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mpc.run = lambda: None
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lead_one = make_lead(status=True, d_rel=100.0, v_lead=20.0)
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lead_two = make_lead(status=True, d_rel=25.0, v_lead=5.0)
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radar_state = SimpleNamespace(leadOne=lead_one, leadTwo=lead_two)
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mpc.update(radar_state, 30.0)
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assert mpc.source == "lead1"
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assert mpc._lead_filter_state[0][0] == pytest.approx(20.0)
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assert mpc._lead_filter_state[1][0] == pytest.approx(5.0)
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lead_one.vLead = 10.0
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mpc.process_lead(lead_one, 0)
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assert mpc._lead_filter_state[1][0] == pytest.approx(5.0)
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def test_fcw_considers_each_credible_closing_lead():
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safe = make_lead(status=True, d_rel=100.0, v_lead=20.0, model_prob=1.0)
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dangerous = make_lead(status=True, d_rel=20.0, v_lead=0.0, model_prob=1.0)
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assert not should_trigger_planner_fcw(safe, 20.0)
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assert should_trigger_planner_fcw(dangerous, 20.0)
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def test_vehicle_tuning_is_declarative_and_honda_exception_is_conservative():
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civic = CarInterface.get_non_essential_params(CAR.HONDA_CIVIC)
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hrv = CarInterface.get_non_essential_params(CAR.HONDA_HRV_3G)
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assert get_longitudinal_planner_tune(hrv).lead_filter_tau > get_longitudinal_planner_tune(civic).lead_filter_tau
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assert get_longitudinal_planner_tune(hrv).accel_slew_rate < get_longitudinal_planner_tune(civic).accel_slew_rate
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def test_gm_pedal_min_accel_keeps_physical_vehicle_floor():
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CP = SimpleNamespace(
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carName=None,
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brand="gm",
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enableGasInterceptorDEPRECATED=True,
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flags=GMFlags.PEDAL_LONG.value,
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carFingerprint=GM_CAR.CHEVROLET_BOLT_ACC_2022_2023_PEDAL,
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)
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assert get_vehicle_min_accel(CP, 32.4) == pytest.approx(-2.95)
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