#!/usr/bin/env python3
import importlib
import math
from collections import defaultdict, deque
import cereal.messaging as messaging
from cereal import car
from common.numpy_fast import interp
from common.params import Params
from common.realtime import Ratekeeper, Priority, config_realtime_process
from selfdrive.config import RADAR_TO_CAMERA
from selfdrive.controls.lib.cluster.fastcluster_py import cluster_points_centroid
from selfdrive.controls.lib.radar_helpers import Cluster, Track
from selfdrive.swaglog import cloudlog
from selfdrive.hardware import TICI
class KalmanParams():
def __init__(self, dt):
# Lead Kalman Filter params, calculating K from A, C, Q, R requires the control library.
# hardcoding a lookup table to compute K for values of radar_ts between 0.01s and 0.2s
assert dt > .01 and dt < .2, "Radar time step must be between .01s and 0.2s"
self.A = [[1.0, dt], [0.0, 1.0]]
self.C = [1.0, 0.0]
#Q = np.matrix([[10., 0.0], [0.0, 100.]])
#R = 1e3
#K = np.matrix([[ 0.05705578], [ 0.03073241]])
dts = [dt * 0.01 for dt in range(1, 21)]
K0 = [0.12287673, 0.14556536, 0.16522756, 0.18281627, 0.1988689, 0.21372394,
0.22761098, 0.24069424, 0.253096, 0.26491023, 0.27621103, 0.28705801,
0.29750003, 0.30757767, 0.31732515, 0.32677158, 0.33594201, 0.34485814,
0.35353899, 0.36200124]
K1 = [0.29666309, 0.29330885, 0.29042818, 0.28787125, 0.28555364, 0.28342219,
0.28144091, 0.27958406, 0.27783249, 0.27617149, 0.27458948, 0.27307714,
0.27162685, 0.27023228, 0.26888809, 0.26758976, 0.26633338, 0.26511557,
0.26393339, 0.26278425]
self.K = [[interp(dt, dts, K0)], [interp(dt, dts, K1)]]
def laplacian_cdf(x, mu, b):
b = max(b, 1e-4)
return math.exp(-abs(x-mu)/b)
def match_vision_to_cluster(v_ego, lead, clusters):
# match vision point to best statistical cluster match
offset_vision_dist = lead.x[0] - RADAR_TO_CAMERA
def prob(c):
prob_d = laplacian_cdf(c.dRel, offset_vision_dist, lead.xStd[0])
prob_y = laplacian_cdf(c.yRel, -lead.y[0], lead.yStd[0])
prob_v = laplacian_cdf(c.vRel + v_ego, lead.v[0], lead.vStd[0])
# This is isn't exactly right, but good heuristic
return prob_d * prob_y * prob_v
cluster = max(clusters, key=prob)
# if no 'sane' match is found return -1
# stationary radar points can be false positives
dist_sane = abs(cluster.dRel - offset_vision_dist) < max([(offset_vision_dist)*.25, 5.0])
vel_sane = (abs(cluster.vRel + v_ego - lead.v[0]) < 10) or (v_ego + cluster.vRel > 3)
if dist_sane and vel_sane:
return cluster
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:
cluster = match_vision_to_cluster(v_ego, lead_msg, clusters)
else:
cluster = None
lead_dict = {'status': False}
if cluster is not None:
lead_dict = cluster.get_RadarState(lead_msg.prob)
elif (cluster 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:
closest_cluster = min(low_speed_clusters, key=lambda c: c.dRel)
# Only choose new cluster if it is actually closer than the previous one
if (not lead_dict['status']) or (closest_cluster.dRel < lead_dict['dRel']):
lead_dict = closest_cluster.get_RadarState()
return lead_dict
class RadarD():
def __init__(self, radar_ts, delay=0):
self.current_time = 0
self.tracks = defaultdict(dict)
self.kalman_params = KalmanParams(radar_ts)
# v_ego
self.v_ego = 0.
self.v_ego_hist = deque([0], maxlen=delay+1)
self.ready = False
def update(self, sm, rr, enable_lead):
self.current_time = 1e-9*max(sm.logMonoTime.values())
if sm.updated['carState']:
self.v_ego = sm['carState'].vEgo
self.v_ego_hist.append(self.v_ego)
if sm.updated['modelV2']:
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 list(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
v_lead = rpt[2] + self.v_ego_hist[0]
# create the track if it doesn't exist or it's a new track
if ids not in self.tracks:
self.tracks[ids] = Track(v_lead, self.kalman_params)
self.tracks[ids].update(rpt[0], rpt[1], rpt[2], v_lead, rpt[3])
idens = list(sorted(self.tracks.keys()))
track_pts = list([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 range(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:
# FIXME: cluster_point_centroid hangs forever if len(track_pts) == 1
cluster_idxs = [0]
clusters = [Cluster()]
clusters[0].add(self.tracks[idens[0]])
else:
clusters = []
# if a new point, reset accel to the rest of the cluster
for idx in range(len(track_pts)):
if self.tracks[idens[idx]].cnt <= 1:
aLeadK = clusters[cluster_idxs[idx]].aLeadK
aLeadTau = clusters[cluster_idxs[idx]].aLeadTau
self.tracks[idens[idx]].reset_a_lead(aLeadK, aLeadTau)
# *** publish radarState ***
dat = messaging.new_message('radarState')
dat.valid = sm.all_alive_and_valid() and len(rr.errors) == 0
radarState = dat.radarState
radarState.mdMonoTime = sm.logMonoTime['modelV2']
radarState.canMonoTimes = list(rr.canMonoTimes)
radarState.radarErrors = list(rr.errors)
radarState.carStateMonoTime = sm.logMonoTime['carState']
if enable_lead:
if len(sm['modelV2'].leadsV3) > 1:
radarState.leadOne = get_lead(self.v_ego, self.ready, clusters, sm['modelV2'].leadsV3[0], low_speed_override=True)
radarState.leadTwo = get_lead(self.v_ego, self.ready, clusters, sm['modelV2'].leadsV3[1], low_speed_override=False)
return dat
# fuses camera and radar data for best lead detection
def radard_thread(sm=None, pm=None, can_sock=None):
config_realtime_process(5 if TICI else 2, Priority.CTRL_LOW)
# 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))
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
# *** setup messaging
if can_sock is None:
can_sock = messaging.sub_sock('can')
if sm is None:
sm = messaging.SubMaster(['modelV2', 'carState'], ignore_avg_freq=['modelV2', 'carState']) # Can't check average frequency, since radar determines timing
if pm is None:
pm = messaging.PubMaster(['radarState', 'liveTracks'])
RI = RadarInterface(CP)
rk = Ratekeeper(1.0 / CP.radarTimeStep, print_delay_threshold=None)
RD = RadarD(CP.radarTimeStep, RI.delay)
# TODO: always log leads once we can hide them conditionally
enable_lead = CP.openpilotLongitudinalControl or 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(sm, rr, enable_lead)
dat.radarState.cumLagMs = -rk.remaining*1000.
pm.send('radarState', dat)
# *** publish tracks for UI debugging (keep last) ***
tracks = RD.tracks
dat = messaging.new_message('liveTracks', len(tracks))
for cnt, ids in enumerate(sorted(tracks.keys())):
dat.liveTracks[cnt] = {
"trackId": ids,
"dRel": float(tracks[ids].dRel),
"yRel": float(tracks[ids].yRel),
"vRel": float(tracks[ids].vRel),
}
pm.send('liveTracks', dat)
rk.monitor_time()
def main(sm=None, pm=None, can_sock=None):
radard_thread(sm, pm, can_sock)
if __name__ == "__main__":
main()