Source code for selfdrive.controls.radard

#!/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.controls.lib.cluster.fastcluster_py import cluster_points_centroid
from selfdrive.controls.lib.radar_helpers import Cluster, Track, RADAR_TO_CAMERA
from system.swaglog import cloudlog


[docs]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)]]
[docs]def laplacian_cdf(x, mu, b): b = max(b, 1e-4) return math.exp(-abs(x-mu)/b)
[docs]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
[docs]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
[docs]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
[docs] def update(self, sm, rr): 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 = [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_checks() 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'] leads_v3 = sm['modelV2'].leadsV3 if len(leads_v3) > 1: radarState.leadOne = get_lead(self.v_ego, self.ready, clusters, leads_v3[0], low_speed_override=True) radarState.leadTwo = get_lead(self.v_ego, self.ready, clusters, leads_v3[1], low_speed_override=False) return dat
# fuses camera and radar data for best lead detection
[docs]def radard_thread(sm=None, pm=None, can_sock=None): config_realtime_process(5, 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(f'selfdrive.car.{CP.carName}.radar_interface').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) 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) 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()
[docs]def main(sm=None, pm=None, can_sock=None): radard_thread(sm, pm, can_sock)
if __name__ == "__main__": main()