jyoung/selfdrive/locationd/torqued.py

#!/usr/bin/env python3
import numpy as np
from collections import deque, defaultdict

import cereal.messaging as messaging
from cereal import car, log
from openpilot.common.params import Params
from openpilot.common.realtime import config_realtime_process, DT_MDL
from openpilot.common.filter_simple import FirstOrderFilter
from openpilot.common.swaglog import cloudlog
from openpilot.selfdrive.controls.lib.vehicle_model import ACCELERATION_DUE_TO_GRAVITY
from openpilot.selfdrive.locationd.helpers import PointBuckets, ParameterEstimator, PoseCalibrator, Pose

HISTORY = 5  # secs
POINTS_PER_BUCKET = 1500
MIN_POINTS_TOTAL = 4000
MIN_POINTS_TOTAL_QLOG = 600
FIT_POINTS_TOTAL = 2000
FIT_POINTS_TOTAL_QLOG = 600
MIN_VEL = 15  # m/s
FRICTION_FACTOR = 1.5  # ~85% of data coverage
FACTOR_SANITY = 0.3
FACTOR_SANITY_QLOG = 0.5
FRICTION_SANITY = 0.5
FRICTION_SANITY_QLOG = 0.8
STEER_MIN_THRESHOLD = 0.02
MIN_FILTER_DECAY = 50
MAX_FILTER_DECAY = 250
LAT_ACC_THRESHOLD = 1
STEER_BUCKET_BOUNDS = [(-0.5, -0.3), (-0.3, -0.2), (-0.2, -0.1), (-0.1, 0), (0, 0.1), (0.1, 0.2), (0.2, 0.3), (0.3, 0.5)]
MIN_BUCKET_POINTS = np.array([100, 300, 500, 500, 500, 500, 300, 100])
MIN_ENGAGE_BUFFER = 2  # secs

VERSION = 1  # bump this to invalidate old parameter caches
ALLOWED_CARS = ['toyota', 'hyundai']


def slope2rot(slope):
  sin = np.sqrt(slope ** 2 / (slope ** 2 + 1))
  cos = np.sqrt(1 / (slope ** 2 + 1))
  return np.array([[cos, -sin], [sin, cos]])


class TorqueBuckets(PointBuckets):
  def add_point(self, x, y):
    for bound_min, bound_max in self.x_bounds:
      if (x >= bound_min) and (x < bound_max):
        self.buckets[(bound_min, bound_max)].append([x, 1.0, y])
        break


class TorqueEstimator(ParameterEstimator):
  def __init__(self, CP, decimated=False, track_all_points=False):
    self.hist_len = int(HISTORY / DT_MDL)
    self.lag = CP.steerActuatorDelay + .2  # from controlsd
    self.track_all_points = track_all_points  # for offline analysis, without max lateral accel or max steer torque filters
    if decimated:
      self.min_bucket_points = MIN_BUCKET_POINTS / 10
      self.min_points_total = MIN_POINTS_TOTAL_QLOG
      self.fit_points = FIT_POINTS_TOTAL_QLOG
      self.factor_sanity = FACTOR_SANITY_QLOG
      self.friction_sanity = FRICTION_SANITY_QLOG

    else:
      self.min_bucket_points = MIN_BUCKET_POINTS
      self.min_points_total = MIN_POINTS_TOTAL
      self.fit_points = FIT_POINTS_TOTAL
      self.factor_sanity = FACTOR_SANITY
      self.friction_sanity = FRICTION_SANITY

    self.offline_friction = 0.0
    self.offline_latAccelFactor = 0.0
    self.resets = 0.0
    self.use_params = CP.carName in ALLOWED_CARS and CP.lateralTuning.which() == 'torque'

    if CP.lateralTuning.which() == 'torque':
      self.offline_friction = CP.lateralTuning.torque.friction
      self.offline_latAccelFactor = CP.lateralTuning.torque.latAccelFactor

    self.calibrator = PoseCalibrator()

    self.reset()

    initial_params = {
      'latAccelFactor': self.offline_latAccelFactor,
      'latAccelOffset': 0.0,
      'frictionCoefficient': self.offline_friction,
      'points': []
    }
    self.decay = MIN_FILTER_DECAY
    self.min_lataccel_factor = (1.0 - self.factor_sanity) * self.offline_latAccelFactor
    self.max_lataccel_factor = (1.0 + self.factor_sanity) * self.offline_latAccelFactor
    self.min_friction = (1.0 - self.friction_sanity) * self.offline_friction
    self.max_friction = (1.0 + self.friction_sanity) * self.offline_friction

    # try to restore cached params
    params = Params()
    params_cache = params.get("CarParamsPrevRoute")
    torque_cache = params.get("LiveTorqueParameters")
    if params_cache is not None and torque_cache is not None:
      try:
        with log.Event.from_bytes(torque_cache) as log_evt:
          cache_ltp = log_evt.liveTorqueParameters
        with car.CarParams.from_bytes(params_cache) as msg:
          cache_CP = msg
        if self.get_restore_key(cache_CP, cache_ltp.version) == self.get_restore_key(CP, VERSION):
          if cache_ltp.liveValid:
            initial_params = {
              'latAccelFactor': cache_ltp.latAccelFactorFiltered,
              'latAccelOffset': cache_ltp.latAccelOffsetFiltered,
              'frictionCoefficient': cache_ltp.frictionCoefficientFiltered
            }
          initial_params['points'] = cache_ltp.points
          self.decay = cache_ltp.decay
          self.filtered_points.load_points(initial_params['points'])
          cloudlog.info("restored torque params from cache")
      except Exception:
        cloudlog.exception("failed to restore cached torque params")
        params.remove("LiveTorqueParameters")

    self.filtered_params = {}
    for param in initial_params:
      self.filtered_params[param] = FirstOrderFilter(initial_params[param], self.decay, DT_MDL)

  @staticmethod
  def get_restore_key(CP, version):
    a, b = None, None
    if CP.lateralTuning.which() == 'torque':
      a = CP.lateralTuning.torque.friction
      b = CP.lateralTuning.torque.latAccelFactor
    return (CP.carFingerprint, CP.lateralTuning.which(), a, b, version)

  def reset(self):
    self.resets += 1.0
    self.decay = MIN_FILTER_DECAY
    self.raw_points = defaultdict(lambda: deque(maxlen=self.hist_len))
    self.filtered_points = TorqueBuckets(x_bounds=STEER_BUCKET_BOUNDS,
                                         min_points=self.min_bucket_points,
                                         min_points_total=self.min_points_total,
                                         points_per_bucket=POINTS_PER_BUCKET,
                                         rowsize=3)
    self.all_torque_points = []

  def estimate_params(self):
    points = self.filtered_points.get_points(self.fit_points)
    # total least square solution as both x and y are noisy observations
    # this is empirically the slope of the hysteresis parallelogram as opposed to the line through the diagonals
    try:
      _, _, v = np.linalg.svd(points, full_matrices=False)
      slope, offset = -v.T[0:2, 2] / v.T[2, 2]
      _, spread = np.matmul(points[:, [0, 2]], slope2rot(slope)).T
      friction_coeff = np.std(spread) * FRICTION_FACTOR
    except np.linalg.LinAlgError as e:
      cloudlog.exception(f"Error computing live torque params: {e}")
      slope = offset = friction_coeff = np.nan
    return slope, offset, friction_coeff

  def update_params(self, params):
    self.decay = min(self.decay + DT_MDL, MAX_FILTER_DECAY)
    for param, value in params.items():
      self.filtered_params[param].update(value)
      self.filtered_params[param].update_alpha(self.decay)

  def handle_log(self, t, which, msg):
    if which == "carControl":
      self.raw_points["carControl_t"].append(t + self.lag)
      self.raw_points["lat_active"].append(msg.latActive)
    elif which == "carOutput":
      self.raw_points["carOutput_t"].append(t + self.lag)
      self.raw_points["steer_torque"].append(-msg.actuatorsOutput.steer)
    elif which == "carState":
      self.raw_points["carState_t"].append(t + self.lag)
      # TODO: check if high aEgo affects resulting lateral accel
      self.raw_points["vego"].append(msg.vEgo)
      self.raw_points["steer_override"].append(msg.steeringPressed)
    elif which == "liveCalibration":
      self.calibrator.feed_live_calib(msg)

    # calculate lateral accel from past steering torque
    elif which == "livePose":
      if len(self.raw_points['steer_torque']) == self.hist_len:
        device_pose = Pose.from_live_pose(msg)
        calibrated_pose = self.calibrator.build_calibrated_pose(device_pose)
        angular_velocity_calibrated = calibrated_pose.angular_velocity

        yaw_rate = angular_velocity_calibrated.yaw
        roll = device_pose.orientation.roll
        # check lat active up to now (without lag compensation)
        lat_active = np.interp(np.arange(t - MIN_ENGAGE_BUFFER, t + self.lag, DT_MDL),
                               self.raw_points['carControl_t'], self.raw_points['lat_active']).astype(bool)
        steer_override = np.interp(np.arange(t - MIN_ENGAGE_BUFFER, t + self.lag, DT_MDL),
                                   self.raw_points['carState_t'], self.raw_points['steer_override']).astype(bool)
        vego = np.interp(t, self.raw_points['carState_t'], self.raw_points['vego'])
        steer = np.interp(t, self.raw_points['carOutput_t'], self.raw_points['steer_torque']).item()
        lateral_acc = (vego * yaw_rate) - (np.sin(roll) * ACCELERATION_DUE_TO_GRAVITY).item()
        if all(lat_active) and not any(steer_override) and (vego > MIN_VEL) and (abs(steer) > STEER_MIN_THRESHOLD):
          if abs(lateral_acc) <= LAT_ACC_THRESHOLD:
            self.filtered_points.add_point(steer, lateral_acc)

          if self.track_all_points:
            self.all_torque_points.append([steer, lateral_acc])

  def get_msg(self, valid=True, with_points=False):
    msg = messaging.new_message('liveTorqueParameters')
    msg.valid = valid
    liveTorqueParameters = msg.liveTorqueParameters
    liveTorqueParameters.version = VERSION
    liveTorqueParameters.useParams = self.use_params

    # Calculate raw estimates when possible, only update filters when enough points are gathered
    if self.filtered_points.is_calculable():
      latAccelFactor, latAccelOffset, frictionCoeff = self.estimate_params()
      liveTorqueParameters.latAccelFactorRaw = float(latAccelFactor)
      liveTorqueParameters.latAccelOffsetRaw = float(latAccelOffset)
      liveTorqueParameters.frictionCoefficientRaw = float(frictionCoeff)

      if self.filtered_points.is_valid():
        if any(val is None or np.isnan(val) for val in [latAccelFactor, latAccelOffset, frictionCoeff]):
          cloudlog.exception("Live torque parameters are invalid.")
          liveTorqueParameters.liveValid = False
          self.reset()
        else:
          liveTorqueParameters.liveValid = True
          latAccelFactor = np.clip(latAccelFactor, self.min_lataccel_factor, self.max_lataccel_factor)
          frictionCoeff = np.clip(frictionCoeff, self.min_friction, self.max_friction)
          self.update_params({'latAccelFactor': latAccelFactor, 'latAccelOffset': latAccelOffset, 'frictionCoefficient': frictionCoeff})

    if with_points:
      liveTorqueParameters.points = self.filtered_points.get_points()[:, [0, 2]].tolist()

    liveTorqueParameters.latAccelFactorFiltered = float(self.filtered_params['latAccelFactor'].x)
    liveTorqueParameters.latAccelOffsetFiltered = float(self.filtered_params['latAccelOffset'].x)
    liveTorqueParameters.frictionCoefficientFiltered = float(self.filtered_params['frictionCoefficient'].x)
    liveTorqueParameters.totalBucketPoints = len(self.filtered_points)
    liveTorqueParameters.decay = self.decay
    liveTorqueParameters.maxResets = self.resets
    return msg


def main(demo=False):
  config_realtime_process([0, 1, 2, 3], 5)

  pm = messaging.PubMaster(['liveTorqueParameters'])
  sm = messaging.SubMaster(['carControl', 'carOutput', 'carState', 'liveCalibration', 'livePose'], poll='livePose')

  params = Params()
  estimator = TorqueEstimator(messaging.log_from_bytes(params.get("CarParams", block=True), car.CarParams))

  while True:
    sm.update()
    if sm.all_checks():
      for which in sm.updated.keys():
        if sm.updated[which]:
          t = sm.logMonoTime[which] * 1e-9
          estimator.handle_log(t, which, sm[which])

    # 4Hz driven by livePose
    if sm.frame % 5 == 0:
      pm.send('liveTorqueParameters', estimator.get_msg(valid=sm.all_checks()))

    # Cache points every 60 seconds while onroad
    if sm.frame % 240 == 0:
      msg = estimator.get_msg(valid=sm.all_checks(), with_points=True)
      params.put_nonblocking("LiveTorqueParameters", msg.to_bytes())


if __name__ == "__main__":
  import argparse

  parser = argparse.ArgumentParser(description='Process the --demo argument.')
  parser.add_argument('--demo', action='store_true', help='A boolean for demo mode.')
  args = parser.parse_args()
  main(demo=args.demo)