openpilot_comma/selfdrive/controls/radard.py

#!/usr/bin/env python
import numpy as np
import numpy.matlib
import importlib
from collections import defaultdict, deque

import selfdrive.messaging as messaging
from selfdrive.services import service_list
from selfdrive.controls.lib.radar_helpers import Track, Cluster
from selfdrive.config import RADAR_TO_CENTER
from selfdrive.controls.lib.cluster.fastcluster_py import cluster_points_centroid
from selfdrive.swaglog import cloudlog
from cereal import car
from common.params import Params
from common.realtime import set_realtime_priority, Ratekeeper, DT_MDL

DEBUG = False

#vision point
DIMSV = 2
XV, SPEEDV = 0, 1
VISION_POINT = -1

# Time-alignment
rate = 1. / DT_MDL  # model and radar are both at 20Hz
v_len = 20   # how many speed data points to remember for t alignment with rdr data


def laplacian_cdf(x, mu, b):
  b = np.max([b, 1e-4])
  return np.exp(-abs(x-mu)/b)


def match_vision_to_cluster(v_ego, lead, clusters):
  # match vision point to best statistical cluster match
  probs = []
  offset_vision_dist = lead.dist - RADAR_TO_CENTER
  for c in clusters:
    prob_d = laplacian_cdf(c.dRel, offset_vision_dist, lead.std)
    prob_y = laplacian_cdf(c.yRel, lead.relY, lead.relYStd)
    prob_v = laplacian_cdf(c.vRel, lead.relVel, lead.relVelStd)
    # This is isn't exactly right, but good heuristic
    combined_prob = prob_d * prob_y * prob_v
    probs.append(combined_prob)
  idx = np.argmax(probs)
  # if no 'sane' match is found return -1
  # stationary radar points can be false positives
  dist_sane = abs(clusters[idx].dRel - offset_vision_dist) < max([(offset_vision_dist)*.25, 5.0])
  vel_sane = (abs(clusters[idx].vRel - lead.relVel) < 10) or (v_ego + clusters[idx].vRel > 2)
  if dist_sane and vel_sane:
    return idx
  else:
    return None


def get_lead(v_ego, ready, clusters, lead_msg, low_speed_override=True):
  # Determine leads, this is where the essential logic happens
  if len(clusters) > 0 and ready and lead_msg.prob > .5:
    lead_idx = match_vision_to_cluster(v_ego, lead_msg, clusters)
  else:
    lead_idx = None

  lead_dict = {'status': False}
  if lead_idx is not None:
    lead_dict = clusters[lead_idx].get_RadarState(lead_msg.prob)
  elif (lead_idx is None) and ready and (lead_msg.prob > .5):
    lead_dict = Cluster().get_RadarState_from_vision(lead_msg, v_ego)

  if low_speed_override:
    low_speed_clusters = [c for c in clusters if c.potential_low_speed_lead(v_ego)]
    if len(low_speed_clusters) > 0:
      lead_idx = np.argmin([c.dRel for c in low_speed_clusters])
      if (not lead_dict['status']) or (low_speed_clusters[lead_idx].dRel < lead_dict['dRel']):
        lead_dict = low_speed_clusters[lead_idx].get_RadarState()

  return lead_dict


class RadarD(object):
  def __init__(self, mocked):
    self.current_time = 0
    self.mocked = mocked

    self.tracks = defaultdict(dict)

    self.last_md_ts = 0
    self.last_controls_state_ts = 0

    self.active = 0

    # v_ego
    self.v_ego = 0.
    self.v_ego_hist_t = deque([0], maxlen=v_len)
    self.v_ego_hist_v = deque([0], maxlen=v_len)
    self.v_ego_t_aligned = 0.
    self.ready = False

  def update(self, frame, delay, sm, rr, has_radar):
    self.current_time = 1e-9*max([sm.logMonoTime[key] for key in sm.logMonoTime.keys()])

    if sm.updated['controlsState']:
      self.active = sm['controlsState'].active
      self.v_ego = sm['controlsState'].vEgo
      self.v_ego_hist_v.append(self.v_ego)
      self.v_ego_hist_t.append(float(frame)/rate)
    if sm.updated['model']:
      self.ready = True

    ar_pts = {}
    for pt in rr.points:
      ar_pts[pt.trackId] = [pt.dRel, pt.yRel, pt.vRel, pt.measured]

    # *** remove missing points from meta data ***
    for ids in self.tracks.keys():
      if ids not in ar_pts:
        self.tracks.pop(ids, None)

    # *** compute the tracks ***
    for ids in ar_pts:
      rpt = ar_pts[ids]

      # align v_ego by a fixed time to align it with the radar measurement
      cur_time = float(frame)/rate
      self.v_ego_t_aligned = np.interp(cur_time - delay, self.v_ego_hist_t, self.v_ego_hist_v)

      # create the track if it doesn't exist or it's a new track
      if ids not in self.tracks:
        self.tracks[ids] = Track()
      self.tracks[ids].update(rpt[0], rpt[1], rpt[2], self.v_ego_t_aligned, rpt[3])

    idens = list(self.tracks.keys())
    track_pts = np.array([self.tracks[iden].get_key_for_cluster() for iden in idens])


    # If we have multiple points, cluster them
    if len(track_pts) > 1:
      cluster_idxs = cluster_points_centroid(track_pts, 2.5)
      clusters = [None] * (max(cluster_idxs) + 1)

      for idx in xrange(len(track_pts)):
        cluster_i = cluster_idxs[idx]
        if clusters[cluster_i] is None:
          clusters[cluster_i] = Cluster()
        clusters[cluster_i].add(self.tracks[idens[idx]])
    elif len(track_pts) == 1:
      clusters = [Cluster()]
      clusters[0].add(self.tracks[idens[0]])
    else:
      clusters = []

    # *** publish radarState ***
    dat = messaging.new_message()
    dat.init('radarState')
    dat.valid = sm.all_alive_and_valid(service_list=['controlsState'])
    dat.radarState.mdMonoTime = self.last_md_ts
    dat.radarState.canMonoTimes = list(rr.canMonoTimes)
    dat.radarState.radarErrors = list(rr.errors)
    dat.radarState.controlsStateMonoTime = self.last_controls_state_ts

    if has_radar:
      dat.radarState.leadOne = get_lead(self.v_ego, self.ready, clusters, sm['model'].lead, low_speed_override=True)
      dat.radarState.leadTwo = get_lead(self.v_ego, self.ready, clusters, sm['model'].leadFuture, low_speed_override=False)
    return dat


# fuses camera and radar data for best lead detection
def radard_thread(gctx=None):
  set_realtime_priority(2)

  # wait for stats about the car to come in from controls
  cloudlog.info("radard is waiting for CarParams")
  CP = car.CarParams.from_bytes(Params().get("CarParams", block=True))
  mocked = CP.carName == "mock"
  cloudlog.info("radard got CarParams")

  # import the radar from the fingerprint
  cloudlog.info("radard is importing %s", CP.carName)
  RadarInterface = importlib.import_module('selfdrive.car.%s.radar_interface' % CP.carName).RadarInterface

  can_sock = messaging.sub_sock(service_list['can'].port)
  sm = messaging.SubMaster(['model', 'controlsState', 'liveParameters'])

  RI = RadarInterface(CP)

  # *** publish radarState and liveTracks
  radarState = messaging.pub_sock(service_list['radarState'].port)
  liveTracks = messaging.pub_sock(service_list['liveTracks'].port)

  rk = Ratekeeper(rate, print_delay_threshold=None)
  RD = RadarD(mocked)

  has_radar = not CP.radarOffCan

  while 1:
    can_strings = messaging.drain_sock_raw(can_sock, wait_for_one=True)
    rr = RI.update(can_strings)

    if rr is None:
      continue

    sm.update(0)

    dat = RD.update(rk.frame, RI.delay, sm, rr, has_radar)
    dat.radarState.cumLagMs = -rk.remaining*1000.

    radarState.send(dat.to_bytes())

    # *** publish tracks for UI debugging (keep last) ***
    tracks = RD.tracks
    dat = messaging.new_message()
    dat.init('liveTracks', len(tracks))

    for cnt, ids in enumerate(tracks.keys()):
      dat.liveTracks[cnt] = {
        "trackId": ids,
        "dRel": float(tracks[ids].dRel),
        "yRel": float(tracks[ids].yRel),
        "vRel": float(tracks[ids].vRel),
      }
    liveTracks.send(dat.to_bytes())

    rk.monitor_time()


def main(gctx=None):
  radard_thread(gctx)


if __name__ == "__main__":
  main()