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306 lines
14 KiB
306 lines
14 KiB
#!/usr/bin/env python3
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import os
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from cereal import car
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from math import fabs, exp
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from panda import Panda
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from openpilot.common.basedir import BASEDIR
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from openpilot.common.conversions import Conversions as CV
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from openpilot.selfdrive.car import create_button_events, get_safety_config
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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, CruiseButtons, 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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from openpilot.selfdrive.controls.lib.drive_helpers import get_friction
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ButtonType = car.CarState.ButtonEvent.Type
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EventName = car.CarEvent.EventName
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GearShifter = car.CarState.GearShifter
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TransmissionType = car.CarParams.TransmissionType
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NetworkLocation = car.CarParams.NetworkLocation
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BUTTONS_DICT = {CruiseButtons.RES_ACCEL: ButtonType.accelCruise, CruiseButtons.DECEL_SET: ButtonType.decelCruise,
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CruiseButtons.MAIN: ButtonType.altButton3, CruiseButtons.CANCEL: ButtonType.cancel}
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NON_LINEAR_TORQUE_PARAMS = {
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CAR.BOLT_EUV: [2.6531724862969748, 1.0, 0.1919764879840985, 0.009054123646805178],
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CAR.ACADIA: [4.78003305, 1.0, 0.3122, 0.05591772],
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CAR.SILVERADO: [3.29974374, 1.0, 0.25571356, 0.0465122]
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}
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NEURAL_PARAMS_PATH = os.path.join(BASEDIR, 'selfdrive/car/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.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: car.CarParams.LateralTorqueTuning, lateral_accel_error: float,
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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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return 1 / (1 + exp(-val)) - 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)
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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: car.CarParams.LateralTorqueTuning, lateral_accel_error: float,
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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.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, candidate, fingerprint, car_fw, experimental_long, docs):
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ret.carName = "gm"
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ret.safetyConfigs = [get_safety_config(car.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.deadzoneBP = [0.]
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ret.longitudinalTuning.deadzoneV = [0.15]
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ret.longitudinalTuning.kpBP = [5., 35.]
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ret.longitudinalTuning.kiBP = [0.]
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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.kpV = [2.0, 1.5]
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ret.longitudinalTuning.kiV = [0.72]
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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.kpV = [2.4, 1.5]
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ret.longitudinalTuning.kiV = [0.36]
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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.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.tireStiffnessFactor = 0.444 # not optimized 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.longitudinalActuatorDelayUpperBound = 0.5 # large delay to initially start braking
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if candidate == CAR.VOLT:
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ret.mass = 1607.
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ret.wheelbase = 2.69
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ret.steerRatio = 17.7 # Stock 15.7, LiveParameters
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ret.tireStiffnessFactor = 0.469 # Stock Michelin Energy Saver A/S, LiveParameters
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ret.centerToFront = ret.wheelbase * 0.45 # Volt Gen 1, TODO corner weigh
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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.MALIBU:
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ret.mass = 1496.
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ret.wheelbase = 2.83
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ret.steerRatio = 15.8
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ret.centerToFront = ret.wheelbase * 0.4 # wild guess
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elif candidate == CAR.HOLDEN_ASTRA:
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ret.mass = 1363.
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ret.wheelbase = 2.662
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# Remaining parameters copied from Volt for now
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ret.centerToFront = ret.wheelbase * 0.4
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ret.steerRatio = 15.7
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elif candidate == CAR.ACADIA:
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ret.minEnableSpeed = -1. # engage speed is decided by pcm
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ret.mass = 4353. * CV.LB_TO_KG
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ret.wheelbase = 2.86
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ret.steerRatio = 14.4 # end to end is 13.46
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ret.centerToFront = ret.wheelbase * 0.4
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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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ret.mass = 1712.
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ret.wheelbase = 2.91
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ret.steerRatio = 15.8
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ret.centerToFront = ret.wheelbase * 0.4 # wild guess
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate == CAR.BUICK_REGAL:
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ret.mass = 3779. * CV.LB_TO_KG # (3849+3708)/2
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ret.wheelbase = 2.83 # 111.4 inches in meters
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ret.steerRatio = 14.4 # guess for tourx
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ret.centerToFront = ret.wheelbase * 0.4 # guess for tourx
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elif candidate == CAR.CADILLAC_ATS:
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ret.mass = 1601.
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ret.wheelbase = 2.78
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ret.steerRatio = 15.3
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ret.centerToFront = ret.wheelbase * 0.5
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elif candidate == CAR.ESCALADE:
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ret.minEnableSpeed = -1. # engage speed is decided by pcm
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ret.mass = 5653. * CV.LB_TO_KG # (5552+5815)/2
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ret.wheelbase = 2.95 # 116 inches in meters
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ret.steerRatio = 17.3
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ret.centerToFront = ret.wheelbase * 0.5
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate in (CAR.ESCALADE_ESV, CAR.ESCALADE_ESV_2019):
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ret.minEnableSpeed = -1. # engage speed is decided by pcm
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ret.mass = 2739.
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ret.wheelbase = 3.302
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ret.steerRatio = 17.3
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ret.centerToFront = ret.wheelbase * 0.5
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ret.tireStiffnessFactor = 1.0
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if candidate == CAR.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.BOLT_EUV:
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ret.mass = 1669.
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ret.wheelbase = 2.63779
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ret.steerRatio = 16.8
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ret.centerToFront = ret.wheelbase * 0.4
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ret.tireStiffnessFactor = 1.0
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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.SILVERADO:
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ret.mass = 2450.
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ret.wheelbase = 3.75
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ret.steerRatio = 16.3
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ret.centerToFront = ret.wheelbase * 0.5
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ret.tireStiffnessFactor = 1.0
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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.EQUINOX:
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ret.mass = 3500. * CV.LB_TO_KG
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ret.wheelbase = 2.72
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ret.steerRatio = 14.4
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ret.centerToFront = ret.wheelbase * 0.4
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CarInterfaceBase.configure_torque_tune(candidate, ret.lateralTuning)
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elif candidate == CAR.TRAILBLAZER:
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ret.mass = 1345.
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ret.wheelbase = 2.64
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ret.steerRatio = 16.8
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ret.centerToFront = ret.wheelbase * 0.4
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ret.tireStiffnessFactor = 1.0
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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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# returns a car.CarState
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def _update(self, c):
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ret = self.CS.update(self.cp, self.cp_cam, self.cp_loopback)
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# Don't add event if transitioning from INIT, unless it's to an actual button
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if self.CS.cruise_buttons != CruiseButtons.UNPRESS or self.CS.prev_cruise_buttons != CruiseButtons.INIT:
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ret.buttonEvents = create_button_events(self.CS.cruise_buttons, self.CS.prev_cruise_buttons, BUTTONS_DICT,
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unpressed_btn=CruiseButtons.UNPRESS)
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# The ECM allows enabling on falling edge of set, but only rising edge of resume
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events = self.create_common_events(ret, extra_gears=[GearShifter.sport, GearShifter.low,
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GearShifter.eco, GearShifter.manumatic],
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pcm_enable=self.CP.pcmCruise, enable_buttons=(ButtonType.decelCruise,))
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if not self.CP.pcmCruise:
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if any(b.type == ButtonType.accelCruise and b.pressed for b in ret.buttonEvents):
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events.add(EventName.buttonEnable)
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# Enabling at a standstill with brake is allowed
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# TODO: verify 17 Volt can enable for the first time at a stop and allow for all GMs
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below_min_enable_speed = ret.vEgo < self.CP.minEnableSpeed or self.CS.moving_backward
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if below_min_enable_speed and not (ret.standstill and ret.brake >= 20 and
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self.CP.networkLocation == NetworkLocation.fwdCamera):
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events.add(EventName.belowEngageSpeed)
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if ret.cruiseState.standstill:
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events.add(EventName.resumeRequired)
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if ret.vEgo < self.CP.minSteerSpeed:
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events.add(EventName.belowSteerSpeed)
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ret.events = events.to_msg()
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return ret
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def apply(self, c, now_nanos):
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return self.CC.update(c, self.CS, now_nanos)
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