StarPilot/selfdrive/controls/lib/latcontrol_torque.py

import math
import numpy as np
from collections import deque

from cereal import log
from openpilot.common.conversions import Conversions as CV
from openpilot.selfdrive.car.interfaces import FRICTION_THRESHOLD, get_friction_threshold
from openpilot.selfdrive.controls.lib.drive_helpers import MIN_SPEED, get_friction
from openpilot.common.filter_simple import FirstOrderFilter
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

# 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 the
# proportional gain is increased at low speeds by the PID controller.
# Additionally, there is friction in the steering wheel that needs
# to be overcome to move it at all, this is compensated for too.

KP = 0.6
KI = 0.3

INTERP_SPEEDS = [1, 1.5, 2.0, 3.0, 5, 7.5, 10, 15, 30]
KP_INTERP = [250, 120, 65, 30, 11.5, 5.5, 3.5, 2.0, KP]

LOW_SPEED_X = [0, 10, 20, 30]
LOW_SPEED_Y = [12, 10.5, 8, 5]
MAX_LAT_JERK_UP = 2.5            # m/s^3

LP_FILTER_CUTOFF_HZ = 1.2
JERK_LOOKAHEAD_SECONDS = 0.19
JERK_GAIN = 0.22
LAT_ACCEL_REQUEST_BUFFER_SECONDS = 1.0
VERSION = 2

class LatControlTorque(LatControl):
  def __init__(self, CP, CI, dt):
    super().__init__(CP, CI, dt)
    self.torque_params = CP.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([INTERP_SPEEDS, KP_INTERP], KI, rate=1/self.dt)
    self.update_limits()
    self.steering_angle_deadzone_deg = self.torque_params.steeringAngleDeadzoneDeg
    self.lat_accel_request_buffer_len = int(LAT_ACCEL_REQUEST_BUFFER_SECONDS / self.dt)
    self.lat_accel_request_buffer = deque([0.] * self.lat_accel_request_buffer_len , maxlen=self.lat_accel_request_buffer_len)
    self.lookahead_frames = int(JERK_LOOKAHEAD_SECONDS / self.dt)
    self.jerk_filter = FirstOrderFilter(0.0, 1 / (2 * np.pi * LP_FILTER_CUTOFF_HZ), self.dt)
    self.previous_measurement = 0.0
    self.measurement_rate_filter = FirstOrderFilter(0.0, 1 / (2 * np.pi * (MAX_LAT_JERK_UP - 0.5)), self.dt)
    self.low_speed_reset_threshold = 7 * CV.MPH_TO_MS

  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()
    pid_log.version = VERSION
    if not active:
      output_torque = 0.0
      pid_log.active = False
      self.pid.reset()
      self.previous_measurement = 0.0
      self.measurement_rate_filter.x = 0.0
      self.lat_accel_request_buffer = deque([0.] * self.lat_accel_request_buffer_len , maxlen=self.lat_accel_request_buffer_len)
    else:
      measured_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))
      lateral_accel_deadzone = curvature_deadzone * CS.vEgo ** 2

      delay_frames = int(np.clip(lat_delay / self.dt, 1, self.lat_accel_request_buffer_len))
      expected_lateral_accel = self.lat_accel_request_buffer[-delay_frames]
      future_desired_lateral_accel = desired_curvature * CS.vEgo ** 2
      self.lat_accel_request_buffer.append(future_desired_lateral_accel)
      raw_lateral_jerk = (future_desired_lateral_accel - expected_lateral_accel) / max(lat_delay, self.dt)
      raw_lateral_jerk = np.clip(raw_lateral_jerk, -MAX_LAT_JERK_UP, MAX_LAT_JERK_UP)
      desired_lateral_jerk = np.clip(self.jerk_filter.update(raw_lateral_jerk), -MAX_LAT_JERK_UP, MAX_LAT_JERK_UP)
      gravity_adjusted_future_lateral_accel = future_desired_lateral_accel - roll_compensation
      setpoint = expected_lateral_accel + desired_lateral_jerk * lat_delay

      measurement = measured_curvature * CS.vEgo ** 2
      measurement_rate = self.measurement_rate_filter.update((measurement - self.previous_measurement) / self.dt)
      measurement_rate = np.clip(measurement_rate, -MAX_LAT_JERK_UP, MAX_LAT_JERK_UP)
      self.previous_measurement = measurement

      low_speed_factor = (np.interp(CS.vEgo, LOW_SPEED_X, LOW_SPEED_Y) / max(CS.vEgo, MIN_SPEED)) ** 2
      current_kp = np.interp(CS.vEgo, self.pid._k_p[0], self.pid._k_p[1])
      error = setpoint - measurement
      error_with_lsf = error * (1 + low_speed_factor / max(current_kp, 1e-3))

      # do error correction in lateral acceleration space, convert at end to handle non-linear torque responses correctly
      pid_log.error = float(error_with_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
      ff += get_friction(error_with_lsf + JERK_GAIN * desired_lateral_jerk, lateral_accel_deadzone, get_friction_threshold(CS.vEgo), self.torque_params)

      if CS.vEgo < self.low_speed_reset_threshold:
        self.pid.reset()
      freeze_integrator = steer_limited_by_safety or CS.steeringPressed or CS.vEgo < self.low_speed_reset_threshold
      output_lataccel = self.pid.update(pid_log.error, error_rate=-measurement_rate, speed=CS.vEgo, feedforward=ff, 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(measurement)
      pid_log.desiredLateralAccel = float(setpoint)
      pid_log.desiredLateralJerk = float(desired_lateral_jerk)
      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