from collections import deque import math import numpy as np from cereal import log from openpilot.common.filter_simple import FirstOrderFilter from openpilot.selfdrive.modeld.constants import ModelConstants from openpilot.selfdrive.controls.lib.drive_helpers import CONTROL_N from opendbc.car.interfaces import LatControlInputs from openpilot.selfdrive.controls.lib.latcontrol import LatControl from openpilot.common.pid import PIDController from openpilot.selfdrive.controls.lib.vehicle_model import ACCELERATION_DUE_TO_GRAVITY from openpilot.common.params import Params # At higher speeds (25+mph) we can assume: # Lateral acceleration achieved by a specific car correlates to # torque applied to the steering rack. It does not correlate to # wheel slip, or to speed. # This controller applies torque to achieve desired lateral # accelerations. To compensate for the low speed effects we # use a LOW_SPEED_FACTOR in the error. Additionally, there is # friction in the steering wheel that needs to be overcome to # move it at all, this is compensated for too. LOW_SPEED_X = [0, 10, 20, 30] LOW_SPEED_Y = [15, 13, 10, 5] LOW_SPEED_Y_NN = [12, 3, 1, 0] LAT_PLAN_MIN_IDX = 5 def get_predicted_lateral_jerk(lat_accels, t_diffs): # compute finite difference between subsequent model_data.acceleration.y values # this is just two calls of np.diff followed by an element-wise division lat_accel_diffs = np.diff(lat_accels) lat_jerk = lat_accel_diffs / t_diffs # return as python list return lat_jerk.tolist() def sign(x): return 1.0 if x > 0.0 else (-1.0 if x < 0.0 else 0.0) def get_lookahead_value(future_vals, current_val): if len(future_vals) == 0: return current_val same_sign_vals = [v for v in future_vals if sign(v) == sign(current_val)] # if any future val has opposite sign of current val, return 0 if len(same_sign_vals) < len(future_vals): return 0.0 # otherwise return the value with minimum absolute value min_val = min(same_sign_vals + [current_val], key=lambda x: abs(x)) return min_val # At a given roll, if pitch magnitude increases, the # gravitational acceleration component starts pointing # in the longitudinal direction, decreasing the lateral # acceleration component. Here we do the same thing # to the roll value itself, then passed to nnff. def roll_pitch_adjust(roll, pitch): return roll * math.cos(pitch) class LatControlTorque(LatControl): def __init__(self, CP, CI): super().__init__(CP, CI) self.torque_params = CP.lateralTuning.torque.as_builder() self.pid = PIDController(self.torque_params.kp, self.torque_params.ki, k_f=self.torque_params.kf, pos_limit=self.steer_max, neg_limit=-self.steer_max) self.torque_from_lateral_accel = CI.torque_from_lateral_accel() self.use_steering_angle = self.torque_params.useSteeringAngle self.steering_angle_deadzone_deg = self.torque_params.steeringAngleDeadzoneDeg # carrot self.frame = 0 self.params = Params() self.lateralTorqueCustom = self.params.get_int("LateralTorqueCustom") self.latAccelFactor_default = self.torque_params.latAccelFactor self.latAccelOffset_default = self.torque_params.latAccelOffset self.friction_default = self.torque_params.friction self.dampingFactor = 0 self.error_last = 0.0 # Twilsonco's Lateral Neural Network Feedforward self.use_nnff = CI.use_nnff self.use_nnff_lite = CI.use_nnff_lite if self.use_nnff or self.use_nnff_lite: # Instantaneous lateral jerk changes very rapidly, making it not useful on its own, # however, we can "look ahead" to the future planned lateral jerk in order to guage # whether the current desired lateral jerk will persist into the future, i.e. # whether it's "deliberate" or not. This lets us simply ignore short-lived jerk. # Note that LAT_PLAN_MIN_IDX is defined above and is used in order to prevent # using a "future" value that is actually planned to occur before the "current" desired # value, which is offset by the steerActuatorDelay. 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 self.friction_look_ahead_bp = [9.0, 30.0] # corresponding speeds in m/s in [0, ~40] in 1.0 increments # Scaling the lateral acceleration "friction response" could be helpful for some. # Increase for a stronger response, decrease for a weaker response. self.lat_jerk_friction_factor = 0.4 self.lat_accel_friction_factor = 0.7 # in [0, 3], in 0.05 increments. 3 is arbitrary safety limit # precompute time differences between ModelConstants.T_IDXS self.t_diffs = np.diff(ModelConstants.T_IDXS) self.desired_lat_jerk_time = self.params.get_float("SteerActuatorDelay") * 0.01 + 0.3 if self.use_nnff: self.pitch = FirstOrderFilter(0.0, 0.5, 0.01) # NN model takes current v_ego, lateral_accel, lat accel/jerk error, roll, and past/future/planned data # of lat accel and roll # Past value is computed using previous desired lat accel and observed roll self.torque_from_nn = CI.get_ff_nn self.nn_friction_override = CI.lat_torque_nn_model.friction_override # setup future time offsets self.nn_time_offset = CP.steerActuatorDelay + 0.2 future_times = [0.3, 0.6, 1.0, 1.5] # seconds in the future self.nn_future_times = [i + self.nn_time_offset for i in future_times] self.nn_future_times_np = np.array(self.nn_future_times) # setup past time offsets self.past_times = [-0.3, -0.2, -0.1] history_check_frames = [int(abs(i)*100) for i in self.past_times] self.history_frame_offsets = [history_check_frames[0] - i for i in history_check_frames] self.lateral_accel_desired_deque = deque(maxlen=history_check_frames[0]) self.roll_deque = deque(maxlen=history_check_frames[0]) self.error_deque = deque(maxlen=history_check_frames[0]) self.past_future_len = len(self.past_times) + len(self.nn_future_times) def update_live_torque_params(self, latAccelFactor, latAccelOffset, friction): if self.lateralTorqueCustom > 0: return self.torque_params.latAccelFactor = latAccelFactor self.torque_params.latAccelOffset = latAccelOffset self.torque_params.friction = friction def update(self, active, CS, VM, params, steer_limited, desired_curvature, llk, model_data=None): self.frame += 1 if self.frame % 10 == 0: lateralTorqueCustom = self.params.get_int("LateralTorqueCustom") self.dampingFactor = self.params.get_float("DampingFactor") * 0.01 if lateralTorqueCustom > 0: self.torque_params.latAccelFactor = self.params.get_float("LateralTorqueAccelFactor")*0.001 self.torque_params.friction = self.params.get_float("LateralTorqueFriction")*0.001 lateralTorqueKp = self.params.get_float("LateralTorqueKpV")*0.01 lateralTorqueKi = self.params.get_float("LateralTorqueKiV")*0.01 lateralTorqueKf = self.params.get_float("LateralTorqueKf")*0.01 lateralTorqueKd = self.params.get_float("LateralTorqueKd")*0.01 self.pid._k_p = [[0], [lateralTorqueKp]] self.pid._k_i = [[0], [lateralTorqueKi]] self.pid.k_f = lateralTorqueKf self.pid._k_d = [[0], [lateralTorqueKd]] self.torque_params.latAccelOffset = self.latAccelOffset_default elif self.lateralTorqueCustom > 1: # 1 -> 0, reset to default self.torque_params.latAccelFactor = self.latAccelFactor_default self.torque_params.friction = self.friction_default self.torque_params.latAccelOffset = self.latAccelOffset_default self.lateralTorqueCustom = lateralTorqueCustom pid_log = log.ControlsState.LateralTorqueState.new_message() nn_log = None steeringRate = math.radians(CS.steeringRateDeg) if not active: output_torque = 0.0 pid_log.active = False angle_steers_des = float(CS.steeringAngleDeg) else: angle_steers_des = math.degrees(VM.get_steer_from_curvature(-desired_curvature, CS.vEgo, params.roll)) angle_steers_des += params.angleOffsetDeg actual_curvature_vm = -VM.calc_curvature(math.radians(CS.steeringAngleDeg - params.angleOffsetDeg), CS.vEgo, params.roll) roll_compensation = params.roll * ACCELERATION_DUE_TO_GRAVITY actual_lateral_jerk = 0.0 if self.use_steering_angle: actual_curvature = actual_curvature_vm curvature_deadzone = abs(VM.calc_curvature(math.radians(self.steering_angle_deadzone_deg), CS.vEgo, 0.0)) if self.use_nnff or self.use_nnff_lite: actual_curvature_rate = -VM.calc_curvature(math.radians(CS.steeringRateDeg), CS.vEgo, 0.0) actual_lateral_jerk = actual_curvature_rate * CS.vEgo ** 2 else: actual_curvature_llk = llk.angularVelocityCalibrated.value[2] / CS.vEgo actual_curvature = interp(CS.vEgo, [2.0, 5.0], [actual_curvature_vm, actual_curvature_llk]) curvature_deadzone = 0.0 desired_lateral_accel = desired_curvature * CS.vEgo ** 2 # desired rate is the desired rate of change in the setpoint, not the absolute desired curvature # desired_lateral_jerk = desired_curvature_rate * CS.vEgo ** 2 actual_lateral_accel = actual_curvature * CS.vEgo ** 2 lateral_accel_deadzone = curvature_deadzone * CS.vEgo ** 2 low_speed_factor = np.interp(CS.vEgo, LOW_SPEED_X, LOW_SPEED_Y)**2 desired_lateral_accel = desired_curvature * CS.vEgo ** 2 setpoint = desired_lateral_accel + low_speed_factor * desired_curvature measurement = actual_lateral_accel + low_speed_factor * actual_curvature lateral_jerk_setpoint = 0 lateral_jerk_measurement = 0 lookahead_lateral_jerk = 0 model_good = model_data is not None and len(model_data.orientation.x) >= CONTROL_N if model_good and (self.use_nnff or self.use_nnff_lite): # prepare "look-ahead" desired lateral jerk lookahead = np.interp(CS.vEgo, self.friction_look_ahead_bp, self.friction_look_ahead_v) friction_upper_idx = next((i for i, val in enumerate(ModelConstants.T_IDXS) if val > lookahead), 16) predicted_lateral_jerk = get_predicted_lateral_jerk(model_data.acceleration.y, self.t_diffs) desired_lateral_jerk = (np.interp(self.desired_lat_jerk_time, ModelConstants.T_IDXS, model_data.acceleration.y) - desired_lateral_accel) / self.desired_lat_jerk_time lookahead_lateral_jerk = get_lookahead_value(predicted_lateral_jerk[LAT_PLAN_MIN_IDX:friction_upper_idx], desired_lateral_jerk) if self.use_steering_angle or lookahead_lateral_jerk == 0.0: lookahead_lateral_jerk = 0.0 actual_lateral_jerk = 0.0 self.lat_accel_friction_factor = 1.0 lateral_jerk_setpoint = self.lat_jerk_friction_factor * lookahead_lateral_jerk lateral_jerk_measurement = self.lat_jerk_friction_factor * actual_lateral_jerk if self.use_nnff and model_good: # update past data pitch = 0 roll = params.roll if len(llk.calibratedOrientationNED.value) > 1: pitch = self.pitch.update(llk.calibratedOrientationNED.value[1]) roll = roll_pitch_adjust(roll, pitch) self.roll_deque.append(roll) self.lateral_accel_desired_deque.append(desired_lateral_accel) # prepare past and future values # adjust future times to account for longitudinal acceleration adjusted_future_times = [t + 0.5*CS.aEgo*(t/max(CS.vEgo, 1.0)) for t in self.nn_future_times] past_rolls = [self.roll_deque[min(len(self.roll_deque)-1, i)] for i in self.history_frame_offsets] future_rolls = [roll_pitch_adjust(np.interp(t, ModelConstants.T_IDXS, model_data.orientation.x) + roll, np.interp(t, ModelConstants.T_IDXS, model_data.orientation.y) + pitch) for t in adjusted_future_times] past_lateral_accels_desired = [self.lateral_accel_desired_deque[min(len(self.lateral_accel_desired_deque)-1, i)] for i in self.history_frame_offsets] future_planned_lateral_accels = [np.interp(t, ModelConstants.T_IDXS, model_data.acceleration.y) for t in adjusted_future_times] # compute NNFF error response nnff_setpoint_input = [CS.vEgo, setpoint, lateral_jerk_setpoint, roll] \ + [setpoint] * self.past_future_len \ + past_rolls + future_rolls # past lateral accel error shouldn't count, so use past desired like the setpoint input nnff_measurement_input = [CS.vEgo, measurement, lateral_jerk_measurement, roll] \ + [measurement] * self.past_future_len \ + past_rolls + future_rolls torque_from_setpoint = self.torque_from_nn(nnff_setpoint_input) torque_from_measurement = self.torque_from_nn(nnff_measurement_input) pid_log.error = float(torque_from_setpoint - torque_from_measurement) error_blend_factor = np.interp(abs(desired_lateral_accel), [1.0, 2.0], [0.0, 1.0]) if error_blend_factor > 0.0: # blend in stronger error response when in high lat accel nnff_error_input = [CS.vEgo, setpoint - measurement, lateral_jerk_setpoint - lateral_jerk_measurement, 0.0] torque_from_error = self.torque_from_nn(nnff_error_input) if sign(pid_log.error) == sign(torque_from_error) and abs(pid_log.error) < abs(torque_from_error): pid_log.error = float(pid_log.error * (1.0 - error_blend_factor) + torque_from_error * error_blend_factor) # compute feedforward (same as nn setpoint output) error = setpoint - measurement friction_input = self.lat_accel_friction_factor * error + self.lat_jerk_friction_factor * lookahead_lateral_jerk nn_input = [CS.vEgo, desired_lateral_accel, friction_input, roll] \ + past_lateral_accels_desired + future_planned_lateral_accels \ + past_rolls + future_rolls ff = self.torque_from_nn(nn_input) # apply friction override for cars with low NN friction response if self.nn_friction_override: pid_log.error += self.torque_from_lateral_accel(LatControlInputs(0.0, 0.0, CS.vEgo, CS.aEgo), self.torque_params, friction_input, lateral_accel_deadzone, friction_compensation=True, gravity_adjusted=False) nn_log = nn_input + nnff_setpoint_input + nnff_measurement_input else: gravity_adjusted_lateral_accel = desired_lateral_accel - roll_compensation torque_from_setpoint = self.torque_from_lateral_accel(LatControlInputs(setpoint, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params, lateral_jerk_setpoint, lateral_accel_deadzone, friction_compensation=self.use_nnff_lite, gravity_adjusted=False) torque_from_measurement = self.torque_from_lateral_accel(LatControlInputs(measurement, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params, lateral_jerk_measurement, lateral_accel_deadzone, friction_compensation=self.use_nnff_lite, gravity_adjusted=False) pid_log.error = float(torque_from_setpoint - torque_from_measurement) error = desired_lateral_accel - actual_lateral_accel if self.use_nnff_lite: friction_input = self.lat_accel_friction_factor * error + self.lat_jerk_friction_factor * lookahead_lateral_jerk else: friction_input = error ff = self.torque_from_lateral_accel(LatControlInputs(gravity_adjusted_lateral_accel, roll_compensation, CS.vEgo, CS.aEgo), self.torque_params, friction_input, lateral_accel_deadzone, friction_compensation=True, gravity_adjusted=True) freeze_integrator = steer_limited or CS.steeringPressed or CS.vEgo < 5 output_torque = self.pid.update(pid_log.error, error_rate=pid_log.error - self.error_last, feedforward=ff, speed=CS.vEgo, freeze_integrator=freeze_integrator) damping_torque = - self.dampingFactor * steeringRate output_torque += damping_torque self.error_last = pid_log.error pid_log.active = True pid_log.p = float(self.pid.p) pid_log.i = float(self.pid.i) pid_log.d = float(self.pid.d) pid_log.f = float(self.pid.f) pid_log.output = float(-output_torque) pid_log.actualLateralAccel = float(actual_lateral_accel) pid_log.desiredLateralAccel = float(desired_lateral_accel) pid_log.saturated = bool(self._check_saturation(self.steer_max - abs(output_torque) < 1e-3, CS, steer_limited)) #if nn_log is not None: # pid_log.nnLog = nn_log # TODO left is positive in this convention return -output_torque,angle_steers_des, pid_log