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194 lines
9.2 KiB
194 lines
9.2 KiB
#!/usr/bin/env python3
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import os
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from math import fabs, exp
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from panda import Panda
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from openpilot.selfdrive.car import get_safety_config, get_friction, structs
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from openpilot.selfdrive.car.common.basedir import BASEDIR
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from openpilot.selfdrive.car.common.conversions import Conversions as CV
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from openpilot.selfdrive.car.gm.radar_interface import RADAR_HEADER_MSG
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from openpilot.selfdrive.car.gm.values import CAR, CarControllerParams, EV_CAR, CAMERA_ACC_CAR, CanBus
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from openpilot.selfdrive.car.interfaces import CarInterfaceBase, TorqueFromLateralAccelCallbackType, FRICTION_THRESHOLD, LatControlInputs, NanoFFModel
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TransmissionType = structs.CarParams.TransmissionType
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NetworkLocation = structs.CarParams.NetworkLocation
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NON_LINEAR_TORQUE_PARAMS = {
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CAR.CHEVROLET_BOLT_EUV: [2.6531724862969748, 1.0, 0.1919764879840985, 0.009054123646805178],
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CAR.GMC_ACADIA: [4.78003305, 1.0, 0.3122, 0.05591772],
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CAR.CHEVROLET_SILVERADO: [3.29974374, 1.0, 0.25571356, 0.0465122]
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}
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NEURAL_PARAMS_PATH = os.path.join(BASEDIR, 'torque_data/neural_ff_weights.json')
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class CarInterface(CarInterfaceBase):
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@staticmethod
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def get_pid_accel_limits(CP, current_speed, cruise_speed):
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return CarControllerParams.ACCEL_MIN, CarControllerParams.ACCEL_MAX
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# Determined by iteratively plotting and minimizing error for f(angle, speed) = steer.
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@staticmethod
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def get_steer_feedforward_volt(desired_angle, v_ego):
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desired_angle *= 0.02904609
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sigmoid = desired_angle / (1 + fabs(desired_angle))
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return 0.10006696 * sigmoid * (v_ego + 3.12485927)
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def get_steer_feedforward_function(self):
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if self.CP.carFingerprint == CAR.CHEVROLET_VOLT:
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return self.get_steer_feedforward_volt
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else:
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return CarInterfaceBase.get_steer_feedforward_default
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def torque_from_lateral_accel_siglin(self, latcontrol_inputs: LatControlInputs, torque_params: structs.CarParams.LateralTorqueTuning,
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lateral_accel_error: float, lateral_accel_deadzone: float, friction_compensation: bool, gravity_adjusted: bool) -> float:
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friction = get_friction(lateral_accel_error, lateral_accel_deadzone, FRICTION_THRESHOLD, torque_params, friction_compensation)
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def sig(val):
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# https://timvieira.github.io/blog/post/2014/02/11/exp-normalize-trick
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if val >= 0:
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return 1 / (1 + exp(-val)) - 0.5
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else:
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z = exp(val)
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return z / (1 + z) - 0.5
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# The "lat_accel vs torque" relationship is assumed to be the sum of "sigmoid + linear" curves
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# An important thing to consider is that the slope at 0 should be > 0 (ideally >1)
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# This has big effect on the stability about 0 (noise when going straight)
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# ToDo: To generalize to other GMs, explore tanh function as the nonlinear
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non_linear_torque_params = NON_LINEAR_TORQUE_PARAMS.get(self.CP.carFingerprint) # type: ignore[call-overload]
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assert non_linear_torque_params, "The params are not defined"
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a, b, c, _ = non_linear_torque_params
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steer_torque = (sig(latcontrol_inputs.lateral_acceleration * a) * b) + (latcontrol_inputs.lateral_acceleration * c)
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return float(steer_torque) + friction
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def torque_from_lateral_accel_neural(self, latcontrol_inputs: LatControlInputs, torque_params: structs.CarParams.LateralTorqueTuning,
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lateral_accel_error: float, lateral_accel_deadzone: float, friction_compensation: bool, gravity_adjusted: bool) -> float:
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friction = get_friction(lateral_accel_error, lateral_accel_deadzone, FRICTION_THRESHOLD, torque_params, friction_compensation)
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inputs = list(latcontrol_inputs)
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if gravity_adjusted:
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inputs[0] += inputs[1]
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return float(self.neural_ff_model.predict(inputs)) + friction
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def torque_from_lateral_accel(self) -> TorqueFromLateralAccelCallbackType:
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if self.CP.carFingerprint == CAR.CHEVROLET_BOLT_EUV:
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self.neural_ff_model = NanoFFModel(NEURAL_PARAMS_PATH, self.CP.carFingerprint)
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return self.torque_from_lateral_accel_neural
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elif self.CP.carFingerprint in NON_LINEAR_TORQUE_PARAMS:
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return self.torque_from_lateral_accel_siglin
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else:
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return self.torque_from_lateral_accel_linear
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@staticmethod
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def _get_params(ret: structs.CarParams, candidate, fingerprint, car_fw, experimental_long, docs) -> structs.CarParams:
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ret.carName = "gm"
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ret.safetyConfigs = [get_safety_config(structs.CarParams.SafetyModel.gm)]
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ret.autoResumeSng = False
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ret.enableBsm = 0x142 in fingerprint[CanBus.POWERTRAIN]
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if candidate in EV_CAR:
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ret.transmissionType = TransmissionType.direct
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else:
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ret.transmissionType = TransmissionType.automatic
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ret.longitudinalTuning.kiBP = [5., 35.]
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if candidate in CAMERA_ACC_CAR:
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ret.experimentalLongitudinalAvailable = True
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ret.networkLocation = NetworkLocation.fwdCamera
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ret.radarUnavailable = True # no radar
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ret.pcmCruise = True
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ret.safetyConfigs[0].safetyParam |= Panda.FLAG_GM_HW_CAM
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ret.minEnableSpeed = 5 * CV.KPH_TO_MS
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ret.minSteerSpeed = 10 * CV.KPH_TO_MS
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# Tuning for experimental long
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ret.longitudinalTuning.kiV = [2.0, 1.5]
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ret.stoppingDecelRate = 2.0 # reach brake quickly after enabling
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ret.vEgoStopping = 0.25
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ret.vEgoStarting = 0.25
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if experimental_long:
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ret.pcmCruise = False
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ret.openpilotLongitudinalControl = True
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ret.safetyConfigs[0].safetyParam |= Panda.FLAG_GM_HW_CAM_LONG
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else: # ASCM, OBD-II harness
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ret.openpilotLongitudinalControl = True
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ret.networkLocation = NetworkLocation.gateway
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ret.radarUnavailable = RADAR_HEADER_MSG not in fingerprint[CanBus.OBSTACLE] and not docs
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ret.pcmCruise = False # stock non-adaptive cruise control is kept off
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# supports stop and go, but initial engage must (conservatively) be above 18mph
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ret.minEnableSpeed = 18 * CV.MPH_TO_MS
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ret.minSteerSpeed = 7 * CV.MPH_TO_MS
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# Tuning
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ret.longitudinalTuning.kiV = [2.4, 1.5]
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# These cars have been put into dashcam only due to both a lack of users and test coverage.
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# These cars likely still work fine. Once a user confirms each car works and a test route is
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# added to selfdrive/car/tests/routes.py, we can remove it from this list.
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ret.dashcamOnly = candidate in {CAR.CADILLAC_ATS, CAR.HOLDEN_ASTRA, CAR.CHEVROLET_MALIBU, CAR.BUICK_REGAL} or \
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(ret.networkLocation == NetworkLocation.gateway and ret.radarUnavailable)
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# Start with a baseline tuning for all GM vehicles. Override tuning as needed in each model section below.
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ret.lateralTuning.pid.kiBP, ret.lateralTuning.pid.kpBP = [[0.], [0.]]
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ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.2], [0.00]]
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ret.lateralTuning.pid.kf = 0.00004 # full torque for 20 deg at 80mph means 0.00007818594
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ret.steerActuatorDelay = 0.1 # Default delay, not measured yet
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ret.steerLimitTimer = 0.4
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ret.radarTimeStep = 0.0667 # GM radar runs at 15Hz instead of standard 20Hz
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ret.longitudinalActuatorDelay = 0.5 # large delay to initially start braking
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if candidate == CAR.CHEVROLET_VOLT:
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ret.lateralTuning.pid.kpBP = [0., 40.]
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ret.lateralTuning.pid.kpV = [0., 0.17]
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ret.lateralTuning.pid.kiBP = [0.]
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ret.lateralTuning.pid.kiV = [0.]
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ret.lateralTuning.pid.kf = 1. # get_steer_feedforward_volt()
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ret.steerActuatorDelay = 0.2
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elif candidate == CAR.GMC_ACADIA:
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ret.minEnableSpeed = -1. # engage speed is decided by pcm
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ret.steerActuatorDelay = 0.2
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate == CAR.BUICK_LACROSSE:
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate == CAR.CADILLAC_ESCALADE:
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ret.minEnableSpeed = -1. # engage speed is decided by pcm
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate in (CAR.CADILLAC_ESCALADE_ESV, CAR.CADILLAC_ESCALADE_ESV_2019):
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ret.minEnableSpeed = -1. # engage speed is decided by pcm
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if candidate == CAR.CADILLAC_ESCALADE_ESV:
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ret.lateralTuning.pid.kiBP, ret.lateralTuning.pid.kpBP = [[10., 41.0], [10., 41.0]]
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ret.lateralTuning.pid.kpV, ret.lateralTuning.pid.kiV = [[0.13, 0.24], [0.01, 0.02]]
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ret.lateralTuning.pid.kf = 0.000045
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else:
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ret.steerActuatorDelay = 0.2
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate == CAR.CHEVROLET_BOLT_EUV:
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ret.steerActuatorDelay = 0.2
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate == CAR.CHEVROLET_SILVERADO:
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# On the Bolt, the ECM and camera independently check that you are either above 5 kph or at a stop
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# with foot on brake to allow engagement, but this platform only has that check in the camera.
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# TODO: check if this is split by EV/ICE with more platforms in the future
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if ret.openpilotLongitudinalControl:
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ret.minEnableSpeed = -1.
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate == CAR.CHEVROLET_EQUINOX:
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate == CAR.CHEVROLET_TRAILBLAZER:
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ret.steerActuatorDelay = 0.2
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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return ret
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