StarPilot/selfdrive/controls/lib/latcontrol_torque.py

import math
import numpy as np
from collections import deque

from cereal import log
from openpilot.selfdrive.controls.lib.drive_helpers import MIN_SPEED, get_friction
from openpilot.selfdrive.car.interfaces import FRICTION_THRESHOLD
from openpilot.selfdrive.controls.lib.latcontrol import LatControl
from openpilot.selfdrive.controls.lib.pid import PIDController
from openpilot.selfdrive.controls.lib.vehicle_model import ACCELERATION_DUE_TO_GRAVITY

from openpilot.frogpilot.controls.lib.neural_network_feedforward import LOW_SPEED_Y_NN, NeuralNetworkFeedforward

# 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]


class LatControlTorque(LatControl):
  def __init__(self, CP, FPCP, CI, dt):
    super().__init__(CP, CI, dt)
    self.torque_params = FPCP.lateralTuning.torque
    self.torque_from_lateral_accel = CI.torque_from_lateral_accel()
    self.lateral_accel_from_torque = CI.lateral_accel_from_torque()
    self.pid = PIDController(self.torque_params.kp, self.torque_params.ki,
                             k_f=self.torque_params.kf, rate=1/self.dt)
    self.update_limits()
    self.steering_angle_deadzone_deg = self.torque_params.steeringAngleDeadzoneDeg
    self.LATACCEL_REQUEST_BUFFER_NUM_FRAMES = int(1 / self.dt)
    self.requested_lateral_accel_buffer = deque([0.] * self.LATACCEL_REQUEST_BUFFER_NUM_FRAMES , maxlen=self.LATACCEL_REQUEST_BUFFER_NUM_FRAMES)

    # FrogPilot variables
    self.nnff = NeuralNetworkFeedforward(CP, self)

    self.nnff_loaded = self.nnff.lat_torque_nn_model != None

  def update_live_torque_params(self, latAccelFactor, latAccelOffset, friction):
    self.torque_params.latAccelFactor = latAccelFactor
    self.torque_params.latAccelOffset = latAccelOffset
    self.torque_params.friction = friction
    self.update_limits()

  def update_limits(self):
    self.pid.set_limits(self.lateral_accel_from_torque(self.steer_max, self.torque_params),
                        self.lateral_accel_from_torque(-self.steer_max, self.torque_params))

  def update(self, active, CS, VM, params, steer_limited_by_safety, desired_curvature, curvature_limited, lat_delay, llk, model_data, frogpilot_toggles):
    pid_log = log.ControlsState.LateralTorqueState.new_message()
    if not active:
      output_torque = 0.0
      pid_log.active = False
    else:
      actual_curvature = -VM.calc_curvature(math.radians(CS.steeringAngleDeg - params.angleOffsetDeg), CS.vEgo, params.roll)
      roll_compensation = params.roll * ACCELERATION_DUE_TO_GRAVITY
      curvature_deadzone = abs(VM.calc_curvature(math.radians(self.steering_angle_deadzone_deg), CS.vEgo, 0.0))

      delay_frames = int(np.clip(lat_delay / self.dt, 1, self.LATACCEL_REQUEST_BUFFER_NUM_FRAMES))
      expected_lateral_accel = self.requested_lateral_accel_buffer[-delay_frames]
      # TODO factor out lateral jerk from error to later replace it with delay independent alternative
      future_desired_lateral_accel = desired_curvature * CS.vEgo ** 2
      self.requested_lateral_accel_buffer.append(future_desired_lateral_accel)
      gravity_adjusted_future_lateral_accel = future_desired_lateral_accel - roll_compensation
      desired_lateral_jerk = (future_desired_lateral_accel - expected_lateral_accel) / lat_delay
      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_NN if frogpilot_toggles.nnff else LOW_SPEED_Y)**2 / (np.clip(CS.vEgo, MIN_SPEED, np.inf) ** 2)
      setpoint = lat_delay * desired_lateral_jerk + expected_lateral_accel
      measurement = actual_lateral_accel
      error = setpoint - measurement
      error_lsf = error + low_speed_factor * error

      if self.nnff_loaded and frogpilot_toggles.nnff or frogpilot_toggles.nnff_lite:
        pid_log, ff = self.nnff.compute_nnff(
          CS, VM, actual_lateral_accel, error, future_desired_lateral_accel, gravity_adjusted_future_lateral_accel,
          llk, measurement, model_data, params, pid_log, setpoint, frogpilot_toggles
        )

        freeze_integrator = steer_limited_by_safety or CS.steeringPressed or CS.vEgo < 5
        output_torque = self.pid.update(pid_log.error,
                                        feedforward=ff,
                                        speed=CS.vEgo,
                                        freeze_integrator=freeze_integrator)
      else:
        # do error correction in lateral acceleration space, convert at end to handle non-linear torque responses correctly
        pid_log.error = float(error_lsf)
        ff = gravity_adjusted_future_lateral_accel
        # latAccelOffset corrects roll compensation bias from device roll misalignment relative to car roll
        ff -= self.torque_params.latAccelOffset
        # TODO jerk is weighted by lat_delay for legacy reasons, but should be made independent of it
        ff += get_friction(error, lateral_accel_deadzone, FRICTION_THRESHOLD, self.torque_params)

        freeze_integrator = steer_limited_by_safety or CS.steeringPressed or CS.vEgo < 5
        output_lataccel = self.pid.update(pid_log.error,
                                        feedforward=ff,
                                        speed=CS.vEgo,
                                        freeze_integrator=freeze_integrator)
        output_torque = self.torque_from_lateral_accel(output_lataccel, self.torque_params)

      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)  # TODO: log lat accel?
      pid_log.actualLateralAccel = float(actual_lateral_accel)
      pid_log.desiredLateralAccel = float(expected_lateral_accel)
      pid_log.saturated = bool(self._check_saturation(self.steer_max - abs(output_torque) < 1e-3, CS, steer_limited_by_safety, curvature_limited))

    # TODO left is positive in this convention
    return -output_torque, 0.0, pid_log