openpilot_comma/selfdrive/controls/lib/radar_helpers.py

from common.kalman.simple_kalman import KF1D
from selfdrive.config import RADAR_TO_CAMERA


# the longer lead decels, the more likely it will keep decelerating
# TODO is this a good default?
_LEAD_ACCEL_TAU = 1.5

# radar tracks
SPEED, ACCEL = 0, 1   # Kalman filter states enum

# stationary qualification parameters
v_ego_stationary = 4.   # no stationary object flag below this speed


class Track():
  def __init__(self, v_lead, kalman_params):
    self.cnt = 0
    self.aLeadTau = _LEAD_ACCEL_TAU
    self.K_A = kalman_params.A
    self.K_C = kalman_params.C
    self.K_K = kalman_params.K
    self.kf = KF1D([[v_lead], [0.0]], self.K_A, self.K_C, self.K_K)

  def update(self, d_rel, y_rel, v_rel, v_lead, measured):
    # relative values, copy
    self.dRel = d_rel   # LONG_DIST
    self.yRel = y_rel   # -LAT_DIST
    self.vRel = v_rel   # REL_SPEED
    self.vLead = v_lead
    self.measured = measured   # measured or estimate

    # computed velocity and accelerations
    if self.cnt > 0:
      self.kf.update(self.vLead)

    self.vLeadK = float(self.kf.x[SPEED][0])
    self.aLeadK = float(self.kf.x[ACCEL][0])

    # Learn if constant acceleration
    if abs(self.aLeadK) < 0.5:
      self.aLeadTau = _LEAD_ACCEL_TAU
    else:
      self.aLeadTau *= 0.9

    self.cnt += 1

  def get_key_for_cluster(self):
    # Weigh y higher since radar is inaccurate in this dimension
    return [self.dRel, self.yRel*2, self.vRel]

  def reset_a_lead(self, aLeadK, aLeadTau):
    self.kf = KF1D([[self.vLead], [aLeadK]], self.K_A, self.K_C, self.K_K)
    self.aLeadK = aLeadK
    self.aLeadTau = aLeadTau

def mean(l):
  return sum(l) / len(l)


class Cluster():
  def __init__(self):
    self.tracks = set()

  def add(self, t):
    # add the first track
    self.tracks.add(t)

  # TODO: make generic
  @property
  def dRel(self):
    return mean([t.dRel for t in self.tracks])

  @property
  def yRel(self):
    return mean([t.yRel for t in self.tracks])

  @property
  def vRel(self):
    return mean([t.vRel for t in self.tracks])

  @property
  def aRel(self):
    return mean([t.aRel for t in self.tracks])

  @property
  def vLead(self):
    return mean([t.vLead for t in self.tracks])

  @property
  def dPath(self):
    return mean([t.dPath for t in self.tracks])

  @property
  def vLat(self):
    return mean([t.vLat for t in self.tracks])

  @property
  def vLeadK(self):
    return mean([t.vLeadK for t in self.tracks])

  @property
  def aLeadK(self):
    if all(t.cnt <= 1 for t in self.tracks):
      return 0.
    else:
      return mean([t.aLeadK for t in self.tracks if t.cnt > 1])

  @property
  def aLeadTau(self):
    if all(t.cnt <= 1 for t in self.tracks):
      return _LEAD_ACCEL_TAU
    else:
      return mean([t.aLeadTau for t in self.tracks if t.cnt > 1])

  @property
  def measured(self):
    return any(t.measured for t in self.tracks)

  def get_RadarState(self, model_prob=0.0):
    return {
      "dRel": float(self.dRel),
      "yRel": float(self.yRel),
      "vRel": float(self.vRel),
      "vLead": float(self.vLead),
      "vLeadK": float(self.vLeadK),
      "aLeadK": float(self.aLeadK),
      "status": True,
      "fcw": self.is_potential_fcw(model_prob),
      "modelProb": model_prob,
      "radar": True,
      "aLeadTau": float(self.aLeadTau)
    }

  def get_RadarState_from_vision(self, lead_msg, v_ego):
    return {
      "dRel": float(lead_msg.dist - RADAR_TO_CAMERA),
      "yRel": float(lead_msg.relY),
      "vRel": float(lead_msg.relVel),
      "vLead": float(v_ego + lead_msg.relVel),
      "vLeadK": float(v_ego + lead_msg.relVel),
      "aLeadK": float(0),
      "aLeadTau": _LEAD_ACCEL_TAU,
      "fcw": False,
      "modelProb": float(lead_msg.prob),
      "radar": False,
      "status": True
    }

  def __str__(self):
    ret = "x: %4.1f  y: %4.1f  v: %4.1f  a: %4.1f" % (self.dRel, self.yRel, self.vRel, self.aLeadK)
    return ret

  def potential_low_speed_lead(self, v_ego):
    # stop for stuff in front of you and low speed, even without model confirmation
    return abs(self.yRel) < 1.5 and (v_ego < v_ego_stationary) and self.dRel < 25

  def is_potential_fcw(self, model_prob):
    return model_prob > .9