IQ.Lvbs CI [bot]
109 lines
3.5 KiB
Python
109 lines
3.5 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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from typing import Any
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from opendbc.car import structs
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from opendbc.car.interfaces import CarInterfaceBase
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from openpilot.common.params import Params
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from openpilot.common.swaglog import cloudlog
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from openpilot.iqpilot.selfdrive.controls.lib.neural_network_feed_forward.locator import get_nn_model_path
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from openpilot.iqpilot.selfdrive.controls.lib.speed_limit.helpers import set_speed_limit_controller_availability
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import openpilot.system.sentry as sentry
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def log_fingerprint(CP: structs.CarParams) -> None:
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if CP.carFingerprint == "MOCK":
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sentry.capture_fingerprint_mock()
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else:
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sentry.capture_fingerprint(CP.carFingerprint, CP.brand)
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def _enforce_torque_lateral_control(CP: structs.CarParams, params: Params = None, enabled: bool = False) -> bool:
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if params is None:
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params = Params()
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if CP.steerControlType != structs.CarParams.SteerControlType.angle:
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enabled = params.get_bool("EnforceTorqueControl")
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return enabled
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def _initialize_neural_network_feed_forward(CP: structs.CarParams, CP_IQ: structs.IQCarParams,
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params: Params = None, enabled: bool = False) -> bool:
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if params is None:
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params = Params()
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nnff_model_path, nnff_model_name, exact_match = get_nn_model_path(CP)
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if nnff_model_name == "MOCK":
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cloudlog.error({"nnff event": "car doesn't match any Neural Network model"})
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if nnff_model_name != "MOCK" and CP.steerControlType != structs.CarParams.SteerControlType.angle:
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enabled = params.get_bool("NeuralNetworkFeedForward")
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CP_IQ.neuralNetworkFeedForward.model.path = nnff_model_path
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CP_IQ.neuralNetworkFeedForward.model.name = nnff_model_name
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CP_IQ.neuralNetworkFeedForward.fuzzyFingerprint = not exact_match
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return enabled
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def _initialize_torque_lateral_control(CI: CarInterfaceBase, CP: structs.CarParams, enforce_torque: bool, nnff_enabled: bool) -> None:
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if nnff_enabled or enforce_torque:
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CI.configure_torque_tune(CP.carFingerprint, CP.lateralTuning)
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def _cleanup_unsupported_params(CP: structs.CarParams, CP_IQ: structs.IQCarParams, params: Params = None) -> None:
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if params is None:
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params = Params()
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if CP.steerControlType == structs.CarParams.SteerControlType.angle:
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cloudlog.warning("SteerControlType is angle, cleaning up params")
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params.remove("NeuralNetworkFeedForward")
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params.remove("EnforceTorqueControl")
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if not CP.openpilotLongitudinalControl and CP_IQ.pcmCruiseSpeed:
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cloudlog.warning("openpilot Longitudinal Control not available, cleaning up params")
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params.remove("CustomAccIncrementsEnabled")
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set_speed_limit_controller_availability(CP, CP_IQ, params)
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def setup_interfaces(CI: CarInterfaceBase, params: Params = None) -> None:
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CP = CI.CP
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CP_IQ = CI.CP_IQ
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enforce_torque = _enforce_torque_lateral_control(CP, params)
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nnff_enabled = _initialize_neural_network_feed_forward(CP, CP_IQ, params)
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_initialize_torque_lateral_control(CI, CP, enforce_torque, nnff_enabled)
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_cleanup_unsupported_params(CP, CP_IQ, params)
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def initialize_params(params) -> list[dict[str, Any]]:
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keys: list = []
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# hyundai
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keys.extend([
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"HyundaiLongitudinalTuning",
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])
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# subaru
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keys.extend([
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"SubaruStopAndGo",
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"SubaruStopAndGoManualParkingBrake",
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])
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# tesla
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keys.extend([
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"TeslaCoopSteering",
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])
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# toyota
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keys.extend([
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"ToyotaEnforceStockLongitudinal",
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])
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return [{k: params.get(k, return_default=True)} for k in keys]
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