246 lines
8.5 KiB
Python
Executable File
246 lines
8.5 KiB
Python
Executable File
#!/usr/bin/env python3
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import importlib
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import math
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from collections import defaultdict, deque
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import cereal.messaging as messaging
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from cereal import car
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from common.numpy_fast import interp
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from common.params import Params
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from common.realtime import Ratekeeper, Priority, config_realtime_process
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from selfdrive.controls.lib.cluster.fastcluster_py import cluster_points_centroid
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from selfdrive.controls.lib.radar_helpers import Cluster, Track, RADAR_TO_CAMERA
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from selfdrive.swaglog import cloudlog
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from selfdrive.hardware import TICI
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class KalmanParams():
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def __init__(self, dt):
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# Lead Kalman Filter params, calculating K from A, C, Q, R requires the control library.
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# hardcoding a lookup table to compute K for values of radar_ts between 0.01s and 0.2s
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assert dt > .01 and dt < .2, "Radar time step must be between .01s and 0.2s"
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self.A = [[1.0, dt], [0.0, 1.0]]
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self.C = [1.0, 0.0]
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#Q = np.matrix([[10., 0.0], [0.0, 100.]])
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#R = 1e3
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#K = np.matrix([[ 0.05705578], [ 0.03073241]])
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dts = [dt * 0.01 for dt in range(1, 21)]
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K0 = [0.12287673, 0.14556536, 0.16522756, 0.18281627, 0.1988689, 0.21372394,
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0.22761098, 0.24069424, 0.253096, 0.26491023, 0.27621103, 0.28705801,
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0.29750003, 0.30757767, 0.31732515, 0.32677158, 0.33594201, 0.34485814,
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0.35353899, 0.36200124]
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K1 = [0.29666309, 0.29330885, 0.29042818, 0.28787125, 0.28555364, 0.28342219,
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0.28144091, 0.27958406, 0.27783249, 0.27617149, 0.27458948, 0.27307714,
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0.27162685, 0.27023228, 0.26888809, 0.26758976, 0.26633338, 0.26511557,
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0.26393339, 0.26278425]
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self.K = [[interp(dt, dts, K0)], [interp(dt, dts, K1)]]
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def laplacian_cdf(x, mu, b):
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b = max(b, 1e-4)
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return math.exp(-abs(x-mu)/b)
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def match_vision_to_cluster(v_ego, lead, clusters):
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# match vision point to best statistical cluster match
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offset_vision_dist = lead.x[0] - RADAR_TO_CAMERA
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def prob(c):
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prob_d = laplacian_cdf(c.dRel, offset_vision_dist, lead.xStd[0])
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prob_y = laplacian_cdf(c.yRel, -lead.y[0], lead.yStd[0])
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prob_v = laplacian_cdf(c.vRel + v_ego, lead.v[0], lead.vStd[0])
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# This is isn't exactly right, but good heuristic
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return prob_d * prob_y * prob_v
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cluster = max(clusters, key=prob)
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# if no 'sane' match is found return -1
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# stationary radar points can be false positives
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dist_sane = abs(cluster.dRel - offset_vision_dist) < max([(offset_vision_dist)*.25, 5.0])
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vel_sane = (abs(cluster.vRel + v_ego - lead.v[0]) < 10) or (v_ego + cluster.vRel > 3)
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if dist_sane and vel_sane:
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return cluster
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else:
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return None
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def get_lead(v_ego, ready, clusters, lead_msg, low_speed_override=True):
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# Determine leads, this is where the essential logic happens
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if len(clusters) > 0 and ready and lead_msg.prob > .5:
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cluster = match_vision_to_cluster(v_ego, lead_msg, clusters)
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else:
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cluster = None
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lead_dict = {'status': False}
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if cluster is not None:
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lead_dict = cluster.get_RadarState(lead_msg.prob)
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elif (cluster is None) and ready and (lead_msg.prob > .5):
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lead_dict = Cluster().get_RadarState_from_vision(lead_msg, v_ego)
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if low_speed_override:
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low_speed_clusters = [c for c in clusters if c.potential_low_speed_lead(v_ego)]
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if len(low_speed_clusters) > 0:
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closest_cluster = min(low_speed_clusters, key=lambda c: c.dRel)
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# Only choose new cluster if it is actually closer than the previous one
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if (not lead_dict['status']) or (closest_cluster.dRel < lead_dict['dRel']):
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lead_dict = closest_cluster.get_RadarState()
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return lead_dict
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class RadarD():
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def __init__(self, radar_ts, delay=0):
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self.current_time = 0
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self.tracks = defaultdict(dict)
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self.kalman_params = KalmanParams(radar_ts)
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# v_ego
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self.v_ego = 0.
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self.v_ego_hist = deque([0], maxlen=delay+1)
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self.ready = False
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def update(self, sm, rr, enable_lead):
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self.current_time = 1e-9*max(sm.logMonoTime.values())
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if sm.updated['carState']:
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self.v_ego = sm['carState'].vEgo
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self.v_ego_hist.append(self.v_ego)
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if sm.updated['modelV2']:
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self.ready = True
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ar_pts = {}
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for pt in rr.points:
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ar_pts[pt.trackId] = [pt.dRel, pt.yRel, pt.vRel, pt.measured]
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# *** remove missing points from meta data ***
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for ids in list(self.tracks.keys()):
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if ids not in ar_pts:
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self.tracks.pop(ids, None)
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# *** compute the tracks ***
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for ids in ar_pts:
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rpt = ar_pts[ids]
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# align v_ego by a fixed time to align it with the radar measurement
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v_lead = rpt[2] + self.v_ego_hist[0]
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# create the track if it doesn't exist or it's a new track
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if ids not in self.tracks:
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self.tracks[ids] = Track(v_lead, self.kalman_params)
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self.tracks[ids].update(rpt[0], rpt[1], rpt[2], v_lead, rpt[3])
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idens = list(sorted(self.tracks.keys()))
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track_pts = [self.tracks[iden].get_key_for_cluster() for iden in idens]
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# If we have multiple points, cluster them
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if len(track_pts) > 1:
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cluster_idxs = cluster_points_centroid(track_pts, 2.5)
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clusters = [None] * (max(cluster_idxs) + 1)
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for idx in range(len(track_pts)):
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cluster_i = cluster_idxs[idx]
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if clusters[cluster_i] is None:
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clusters[cluster_i] = Cluster()
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clusters[cluster_i].add(self.tracks[idens[idx]])
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elif len(track_pts) == 1:
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# FIXME: cluster_point_centroid hangs forever if len(track_pts) == 1
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cluster_idxs = [0]
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clusters = [Cluster()]
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clusters[0].add(self.tracks[idens[0]])
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else:
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clusters = []
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# if a new point, reset accel to the rest of the cluster
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for idx in range(len(track_pts)):
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if self.tracks[idens[idx]].cnt <= 1:
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aLeadK = clusters[cluster_idxs[idx]].aLeadK
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aLeadTau = clusters[cluster_idxs[idx]].aLeadTau
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self.tracks[idens[idx]].reset_a_lead(aLeadK, aLeadTau)
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# *** publish radarState ***
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dat = messaging.new_message('radarState')
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dat.valid = sm.all_alive_and_valid() and len(rr.errors) == 0
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radarState = dat.radarState
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radarState.mdMonoTime = sm.logMonoTime['modelV2']
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radarState.canMonoTimes = list(rr.canMonoTimes)
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radarState.radarErrors = list(rr.errors)
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radarState.carStateMonoTime = sm.logMonoTime['carState']
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if enable_lead:
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leads_v3 = sm['modelV2'].leadsV3
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if len(leads_v3) > 1:
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radarState.leadOne = get_lead(self.v_ego, self.ready, clusters, leads_v3[0], low_speed_override=True)
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radarState.leadTwo = get_lead(self.v_ego, self.ready, clusters, leads_v3[1], low_speed_override=False)
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return dat
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# fuses camera and radar data for best lead detection
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def radard_thread(sm=None, pm=None, can_sock=None):
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config_realtime_process(5 if TICI else 2, Priority.CTRL_LOW)
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# wait for stats about the car to come in from controls
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cloudlog.info("radard is waiting for CarParams")
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CP = car.CarParams.from_bytes(Params().get("CarParams", block=True))
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cloudlog.info("radard got CarParams")
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# import the radar from the fingerprint
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cloudlog.info("radard is importing %s", CP.carName)
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RadarInterface = importlib.import_module(f'selfdrive.car.{CP.carName}.radar_interface').RadarInterface
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# *** setup messaging
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if can_sock is None:
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can_sock = messaging.sub_sock('can')
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if sm is None:
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sm = messaging.SubMaster(['modelV2', 'carState'], ignore_avg_freq=['modelV2', 'carState']) # Can't check average frequency, since radar determines timing
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if pm is None:
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pm = messaging.PubMaster(['radarState', 'liveTracks'])
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RI = RadarInterface(CP)
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rk = Ratekeeper(1.0 / CP.radarTimeStep, print_delay_threshold=None)
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RD = RadarD(CP.radarTimeStep, RI.delay)
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# TODO: always log leads once we can hide them conditionally
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enable_lead = CP.openpilotLongitudinalControl or not CP.radarOffCan
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while 1:
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can_strings = messaging.drain_sock_raw(can_sock, wait_for_one=True)
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rr = RI.update(can_strings)
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if rr is None:
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continue
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sm.update(0)
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dat = RD.update(sm, rr, enable_lead)
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dat.radarState.cumLagMs = -rk.remaining*1000.
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pm.send('radarState', dat)
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# *** publish tracks for UI debugging (keep last) ***
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tracks = RD.tracks
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dat = messaging.new_message('liveTracks', len(tracks))
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for cnt, ids in enumerate(sorted(tracks.keys())):
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dat.liveTracks[cnt] = {
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"trackId": ids,
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"dRel": float(tracks[ids].dRel),
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"yRel": float(tracks[ids].yRel),
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"vRel": float(tracks[ids].vRel),
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}
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pm.send('liveTracks', dat)
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rk.monitor_time()
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def main(sm=None, pm=None, can_sock=None):
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radard_thread(sm, pm, can_sock)
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if __name__ == "__main__":
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main()
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