59 lines
2.6 KiB
Python
59 lines
2.6 KiB
Python
"""
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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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import os
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from math import exp
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from iqdbc.car import structs
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from iqdbc.car.common.basedir import BASEDIR
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from iqdbc.car.gm.interface import CAR
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from iqdbc.iqpilot.car.interfaces import LatControlInputs, NanoFFModel, TorqueFromLateralAccelCallbackTypeTorqueSpace
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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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class CarInterfaceExt:
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def __init__(self, CP: structs.CarParams, CI_Base):
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self.CP = CP
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self.CI_Base = CI_Base
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self.neural_ff_model = None
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def torque_from_lateral_accel_siglin(self, latcontrol_inputs: LatControlInputs, torque_params: structs.CarParams.LateralTorqueTuning,
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gravity_adjusted: bool) -> float:
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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)
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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)
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def torque_from_lateral_accel_neural(self, latcontrol_inputs: LatControlInputs, orque_params: structs.CarParams.LateralTorqueTuning,
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gravity_adjusted: bool) -> float:
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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))
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def torque_from_lateral_accel_in_torque_space(self) -> TorqueFromLateralAccelCallbackTypeTorqueSpace:
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if self.CP.carFingerprint == CAR.CHEVROLET_BOLT_EUV:
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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.CI_Base.torque_from_lateral_accel_linear_in_torque_space
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