133 lines
5.8 KiB
Python
133 lines
5.8 KiB
Python
"""
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Copyright (c) 2021-, Haibin Wen, sunnypilot, and a number of other contributors.
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This file is part of sunnypilot and is licensed under the MIT License.
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See the LICENSE.md file in the root directory for more details.
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"""
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import math
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import numpy as np
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from openpilot.common.pid import PIDController
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from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N
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from openpilot.selfdrive.modeld.constants import ModelConstants
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LAT_PLAN_MIN_IDX = 5
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LATERAL_LAG_MOD = 0.0 # seconds, modifies how far in the future we look ahead for the lateral plan
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def get_predicted_lateral_jerk(lat_accels, t_diffs):
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# compute finite difference between subsequent model_v2.acceleration.y values
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# this is just two calls of np.diff followed by an element-wise division
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lat_accel_diffs = np.diff(lat_accels)
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lat_jerk = lat_accel_diffs / t_diffs
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# return as python list
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return lat_jerk.tolist()
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def sign(x):
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return 1.0 if x > 0.0 else (-1.0 if x < 0.0 else 0.0)
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def get_lookahead_value(future_vals, current_val):
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if len(future_vals) == 0:
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return current_val
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same_sign_vals = [v for v in future_vals if sign(v) == sign(current_val)]
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# if any future val has opposite sign of current val, return 0
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if len(same_sign_vals) < len(future_vals):
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return 0.0
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# otherwise return the value with minimum absolute value
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min_val = min(same_sign_vals + [current_val], key=lambda x: abs(x))
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return min_val
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class LatControlTorqueExtBase:
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def __init__(self, lac_torque, CP, CP_SP, CI):
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self.model_v2 = None
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self.model_valid = False
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self.lac_torque = lac_torque
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self.torque_params = lac_torque.torque_params
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self.actual_lateral_jerk: float = 0.0
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self.lateral_jerk_setpoint: float = 0.0
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self.lateral_jerk_measurement: float = 0.0
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self.lookahead_lateral_jerk: float = 0.0
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self.torque_from_lateral_accel_in_torque_space = CI.torque_from_lateral_accel_in_torque_space()
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self._ff = 0.0
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self._pid = PIDController(self.torque_params.kp, self.torque_params.ki,
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k_f=self.torque_params.kf)
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self._pid_log = None
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self._setpoint = 0.0
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self._measurement = 0.0
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self._roll_compensation = 0.0
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self._lateral_accel_deadzone = 0.0
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self._desired_lateral_accel = 0.0
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self._actual_lateral_accel = 0.0
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self._desired_curvature = 0.0
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self._actual_curvature = 0.0
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self._gravity_adjusted_lateral_accel = 0.0
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self._steer_limited_by_safety = False
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self._output_torque = 0.0
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# twilsonco's Lateral Neural Network Feedforward
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# Instantaneous lateral jerk changes very rapidly, making it not useful on its own,
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# however, we can "look ahead" to the future planned lateral jerk in order to gauge
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# whether the current desired lateral jerk will persist into the future, i.e.
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# whether it's "deliberate" or not. This allows us to simply ignore short-lived jerk.
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# Note that LAT_PLAN_MIN_IDX is defined above and is used in order to prevent
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# using a "future" value that is actually planned to occur before the "current" desired
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# value, which is offset by the steerActuatorDelay.
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# TODO-SP: Reevaluate lookahead v values that determines how low a desired lateral jerk signal needs to
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# persist in order to be used.
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self.friction_look_ahead_v = [1.4, 2.0] # how many seconds in the future to look ahead in [0, ~2.1] in 0.1 increments
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self.friction_look_ahead_bp = [9.0, 30.0] # corresponding speeds in m/s in [0, ~40] in 1.0 increments
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# Scaling the lateral acceleration "friction response" could be helpful for some.
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# Increase for a stronger response, decrease for a weaker response.
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self.lat_jerk_friction_factor = 0.4
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self.lat_accel_friction_factor = 0.7 # in [0, 3], in 0.05 increments. 3 is arbitrary safety limit
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# precompute time differences between ModelConstants.T_IDXS
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self.t_diffs = np.diff(ModelConstants.T_IDXS)
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self.desired_lat_jerk_time = CP.steerActuatorDelay + LATERAL_LAG_MOD
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def update_model_v2(self, model_v2):
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self.model_v2 = model_v2
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self.model_valid = self.model_v2 is not None and len(self.model_v2.orientation.x) >= CONTROL_N
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def update_lateral_lag(self, lag):
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self.desired_lat_jerk_time = max(0.01, lag) + LATERAL_LAG_MOD
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def update_friction_input(self, val_1, val_2):
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_error = val_1 - val_2
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_value = self.lat_accel_friction_factor * _error + self.lat_jerk_friction_factor * self.lookahead_lateral_jerk
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return _value
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def update_calculations(self, CS, VM, desired_lateral_accel):
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self.actual_lateral_jerk = 0.0
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self.lateral_jerk_setpoint = 0.0
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self.lateral_jerk_measurement = 0.0
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self.lookahead_lateral_jerk = 0.0
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actual_curvature_rate = -VM.calc_curvature(math.radians(CS.steeringRateDeg), CS.vEgo, 0.0)
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self.actual_lateral_jerk = actual_curvature_rate * CS.vEgo ** 2
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if self.model_valid:
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# prepare "look-ahead" desired lateral jerk
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lookahead = np.interp(CS.vEgo, self.friction_look_ahead_bp, self.friction_look_ahead_v)
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friction_upper_idx = next((i for i, val in enumerate(ModelConstants.T_IDXS) if val > lookahead), 16)
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predicted_lateral_jerk = get_predicted_lateral_jerk(self.model_v2.acceleration.y, self.t_diffs)
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desired_lateral_jerk = (np.interp(self.desired_lat_jerk_time, ModelConstants.T_IDXS,
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self.model_v2.acceleration.y) - desired_lateral_accel) / self.desired_lat_jerk_time
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self.lookahead_lateral_jerk = get_lookahead_value(predicted_lateral_jerk[LAT_PLAN_MIN_IDX:friction_upper_idx], desired_lateral_jerk)
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if self.lookahead_lateral_jerk == 0.0:
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self.actual_lateral_jerk = 0.0
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self.lat_accel_friction_factor = 1.0
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self.lateral_jerk_setpoint = self.lat_jerk_friction_factor * self.lookahead_lateral_jerk
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self.lateral_jerk_measurement = self.lat_jerk_friction_factor * self.actual_lateral_jerk
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