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315 lines
11 KiB
315 lines
11 KiB
#!/usr/bin/env python
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import zmq
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import numpy as np
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import numpy.matlib
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import importlib
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from collections import defaultdict, deque
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from fastcluster import linkage_vector
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import selfdrive.messaging as messaging
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from selfdrive.services import service_list
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from selfdrive.controls.lib.latcontrol_helpers import calc_lookahead_offset
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from selfdrive.controls.lib.model_parser import ModelParser
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from selfdrive.controls.lib.radar_helpers import Track, Cluster, fcluster, \
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RDR_TO_LDR, NO_FUSION_SCORE
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from selfdrive.controls.lib.vehicle_model import VehicleModel
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from selfdrive.swaglog import cloudlog
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from cereal import car
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from common.params import Params
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from common.realtime import set_realtime_priority, Ratekeeper
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from common.kalman.ekf import EKF, SimpleSensor
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DEBUG = False
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#vision point
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DIMSV = 2
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XV, SPEEDV = 0, 1
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VISION_POINT = -1
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class EKFV1D(EKF):
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def __init__(self):
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super(EKFV1D, self).__init__(False)
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self.identity = numpy.matlib.identity(DIMSV)
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self.state = np.matlib.zeros((DIMSV, 1))
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self.var_init = 1e2 # ~ model variance when probability is 70%, so good starting point
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self.covar = self.identity * self.var_init
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self.process_noise = np.matlib.diag([0.5, 1])
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def calc_transfer_fun(self, dt):
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tf = np.matlib.identity(DIMSV)
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tf[XV, SPEEDV] = dt
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tfj = tf
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return tf, tfj
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## fuses camera and radar data for best lead detection
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# FIXME: radard has a memory leak of about 50MB/hr
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# BOUNTY: $100 coupon on shop.comma.ai
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def radard_thread(gctx=None):
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set_realtime_priority(2)
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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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mocked = CP.carName == "mock"
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VM = VehicleModel(CP)
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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('selfdrive.car.%s.radar_interface' % CP.carName).RadarInterface
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context = zmq.Context()
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# *** subscribe to features and model from visiond
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poller = zmq.Poller()
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model = messaging.sub_sock(context, service_list['model'].port, conflate=True, poller=poller)
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live100 = messaging.sub_sock(context, service_list['live100'].port, conflate=True, poller=poller)
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live_parameters_sock = messaging.sub_sock(context, service_list['liveParameters'].port, conflate=True, poller=poller)
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# Default parameters
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live_parameters = messaging.new_message()
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live_parameters.init('liveParameters')
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live_parameters.liveParameters.valid = True
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live_parameters.liveParameters.steerRatio = CP.steerRatio
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live_parameters.liveParameters.stiffnessFactor = 1.0
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MP = ModelParser()
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RI = RadarInterface(CP)
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last_md_ts = 0
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last_l100_ts = 0
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# *** publish live20 and liveTracks
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live20 = messaging.pub_sock(context, service_list['live20'].port)
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liveTracks = messaging.pub_sock(context, service_list['liveTracks'].port)
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path_x = np.arange(0.0, 140.0, 0.1) # 140 meters is max
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# Time-alignment
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rate = 20. # model and radar are both at 20Hz
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tsv = 1./rate
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v_len = 20 # how many speed data points to remember for t alignment with rdr data
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active = 0
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steer_angle = 0.
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steer_override = False
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tracks = defaultdict(dict)
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# Kalman filter stuff:
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ekfv = EKFV1D()
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speedSensorV = SimpleSensor(XV, 1, 2)
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# v_ego
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v_ego = None
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v_ego_hist_t = deque(maxlen=v_len)
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v_ego_hist_v = deque(maxlen=v_len)
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v_ego_t_aligned = 0.
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rk = Ratekeeper(rate, print_delay_threshold=np.inf)
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while 1:
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rr = RI.update()
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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 + RDR_TO_LDR, pt.yRel, pt.vRel, pt.measured]
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# receive the live100s
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l100 = None
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md = None
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for socket, event in poller.poll(0):
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if socket is live100:
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l100 = messaging.recv_one(socket)
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elif socket is model:
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md = messaging.recv_one(socket)
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elif socket is live_parameters_sock:
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live_parameters = messaging.recv_one(socket)
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VM.update_params(live_parameters.liveParameters.stiffnessFactor, live_parameters.liveParameters.steerRatio)
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if l100 is not None:
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active = l100.live100.active
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v_ego = l100.live100.vEgo
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steer_angle = l100.live100.angleSteers
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steer_override = l100.live100.steerOverride
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v_ego_hist_v.append(v_ego)
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v_ego_hist_t.append(float(rk.frame)/rate)
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last_l100_ts = l100.logMonoTime
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if v_ego is None:
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continue
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if md is not None:
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last_md_ts = md.logMonoTime
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# *** get path prediction from the model ***
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MP.update(v_ego, md)
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# run kalman filter only if prob is high enough
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if MP.lead_prob > 0.7:
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reading = speedSensorV.read(MP.lead_dist, covar=np.matrix(MP.lead_var))
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ekfv.update_scalar(reading)
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ekfv.predict(tsv)
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# When changing lanes the distance to the lead car can suddenly change,
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# which makes the Kalman filter output large relative acceleration
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if mocked and abs(MP.lead_dist - ekfv.state[XV]) > 2.0:
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ekfv.state[XV] = MP.lead_dist
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ekfv.covar = (np.diag([MP.lead_var, ekfv.var_init]))
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ekfv.state[SPEEDV] = 0.
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ar_pts[VISION_POINT] = (float(ekfv.state[XV]), np.polyval(MP.d_poly, float(ekfv.state[XV])),
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float(ekfv.state[SPEEDV]), False)
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else:
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ekfv.state[XV] = MP.lead_dist
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ekfv.covar = (np.diag([MP.lead_var, ekfv.var_init]))
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ekfv.state[SPEEDV] = 0.
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if VISION_POINT in ar_pts:
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del ar_pts[VISION_POINT]
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# *** compute the likely path_y ***
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if (active and not steer_override) or mocked:
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# use path from model (always when mocking as steering is too noisy)
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path_y = np.polyval(MP.d_poly, path_x)
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else:
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# use path from steer, set angle_offset to 0 it does not only report the physical offset
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path_y = calc_lookahead_offset(v_ego, steer_angle, path_x, VM, angle_offset=live_parameters.liveParameters.angleOffsetAverage)[0]
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# *** remove missing points from meta data ***
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for ids in tracks.keys():
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if ids not in ar_pts:
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tracks.pop(ids, None)
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# *** compute the tracks ***
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for ids in ar_pts:
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# ignore standalone vision point, unless we are mocking the radar
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if ids == VISION_POINT and not mocked:
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continue
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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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cur_time = float(rk.frame)/rate
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v_ego_t_aligned = np.interp(cur_time - RI.delay, v_ego_hist_t, v_ego_hist_v)
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d_path = np.sqrt(np.amin((path_x - rpt[0]) ** 2 + (path_y - rpt[1]) ** 2))
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# add sign
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d_path *= np.sign(rpt[1] - np.interp(rpt[0], path_x, path_y))
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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 tracks:
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tracks[ids] = Track()
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tracks[ids].update(rpt[0], rpt[1], rpt[2], d_path, v_ego_t_aligned, rpt[3], steer_override)
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# allow the vision model to remove the stationary flag if distance and rel speed roughly match
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if VISION_POINT in ar_pts:
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fused_id = None
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best_score = NO_FUSION_SCORE
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for ids in tracks:
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dist_to_vision = np.sqrt((0.5*(ar_pts[VISION_POINT][0] - tracks[ids].dRel)) ** 2 + (2*(ar_pts[VISION_POINT][1] - tracks[ids].yRel)) ** 2)
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rel_speed_diff = abs(ar_pts[VISION_POINT][2] - tracks[ids].vRel)
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tracks[ids].update_vision_score(dist_to_vision, rel_speed_diff)
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if best_score > tracks[ids].vision_score:
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fused_id = ids
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best_score = tracks[ids].vision_score
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if fused_id is not None:
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tracks[fused_id].vision_cnt += 1
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tracks[fused_id].update_vision_fusion()
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if DEBUG:
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print("NEW CYCLE")
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if VISION_POINT in ar_pts:
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print("vision", ar_pts[VISION_POINT])
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idens = tracks.keys()
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track_pts = np.array([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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link = linkage_vector(track_pts, method='centroid')
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cluster_idxs = fcluster(link, 2.5, criterion='distance')
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clusters = [None]*max(cluster_idxs)
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for idx in xrange(len(track_pts)):
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cluster_i = cluster_idxs[idx]-1
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if clusters[cluster_i] == None:
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clusters[cluster_i] = Cluster()
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clusters[cluster_i].add(tracks[idens[idx]])
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elif len(track_pts) == 1:
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# TODO: why do we need this?
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clusters = [Cluster()]
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clusters[0].add(tracks[idens[0]])
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else:
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clusters = []
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if DEBUG:
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for i in clusters:
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print(i)
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# *** extract the lead car ***
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lead_clusters = [c for c in clusters
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if c.is_potential_lead(v_ego)]
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lead_clusters.sort(key=lambda x: x.dRel)
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lead_len = len(lead_clusters)
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# *** extract the second lead from the whole set of leads ***
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lead2_clusters = [c for c in lead_clusters
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if c.is_potential_lead2(lead_clusters)]
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lead2_clusters.sort(key=lambda x: x.dRel)
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lead2_len = len(lead2_clusters)
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# *** publish live20 ***
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dat = messaging.new_message()
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dat.init('live20')
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dat.live20.mdMonoTime = last_md_ts
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dat.live20.canMonoTimes = list(rr.canMonoTimes)
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dat.live20.radarErrors = list(rr.errors)
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dat.live20.l100MonoTime = last_l100_ts
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if lead_len > 0:
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dat.live20.leadOne = lead_clusters[0].toLive20()
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if lead2_len > 0:
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dat.live20.leadTwo = lead2_clusters[0].toLive20()
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else:
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dat.live20.leadTwo.status = False
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else:
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dat.live20.leadOne.status = False
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dat.live20.cumLagMs = -rk.remaining*1000.
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live20.send(dat.to_bytes())
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# *** publish tracks for UI debugging (keep last) ***
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dat = messaging.new_message()
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dat.init('liveTracks', len(tracks))
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for cnt, ids in enumerate(tracks.keys()):
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if DEBUG:
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print("id: %4.0f x: %4.1f y: %4.1f vr: %4.1f d: %4.1f va: %4.1f vl: %4.1f vlk: %4.1f alk: %4.1f s: %1.0f v: %1.0f" % \
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(ids, tracks[ids].dRel, tracks[ids].yRel, tracks[ids].vRel,
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tracks[ids].dPath, tracks[ids].vLat,
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tracks[ids].vLead, tracks[ids].vLeadK,
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tracks[ids].aLeadK,
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tracks[ids].stationary,
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tracks[ids].measured))
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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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"aRel": float(tracks[ids].aRel),
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"stationary": bool(tracks[ids].stationary),
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"oncoming": bool(tracks[ids].oncoming),
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}
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liveTracks.send(dat.to_bytes())
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rk.monitor_time()
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def main(gctx=None):
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radard_thread(gctx)
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if __name__ == "__main__":
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main()
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