381 lines
13 KiB
Python
Executable File
381 lines
13 KiB
Python
Executable File
#!/usr/bin/env python3
|
|
import importlib
|
|
import math
|
|
from collections import deque
|
|
from typing import Any
|
|
|
|
import capnp
|
|
from cereal import messaging, log, car
|
|
from openpilot.common.numpy_fast import interp
|
|
from openpilot.common.params import Params
|
|
from openpilot.common.realtime import DT_CTRL, Ratekeeper, Priority, config_realtime_process
|
|
from openpilot.common.swaglog import cloudlog
|
|
|
|
from openpilot.common.simple_kalman import KF1D
|
|
|
|
from openpilot.selfdrive.frogpilot.controls.lib.frogpilot_variables import FrogPilotVariables
|
|
|
|
# Default lead acceleration decay set to 50% at 1s
|
|
_LEAD_ACCEL_TAU = 1.5
|
|
|
|
# radar tracks
|
|
SPEED, ACCEL = 0, 1 # Kalman filter states enum
|
|
|
|
# stationary qualification parameters
|
|
V_EGO_STATIONARY = 4. # no stationary object flag below this speed
|
|
|
|
RADAR_TO_CENTER = 2.7 # (deprecated) RADAR is ~ 2.7m ahead from center of car
|
|
RADAR_TO_CAMERA = 1.52 # RADAR is ~ 1.5m ahead from center of mesh frame
|
|
|
|
|
|
class KalmanParams:
|
|
def __init__(self, dt: float):
|
|
# 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)]]
|
|
|
|
|
|
class Track:
|
|
def __init__(self, identifier: int, v_lead: float, kalman_params: KalmanParams):
|
|
self.identifier = identifier
|
|
self.cnt = 0
|
|
self.aLeadTau = _LEAD_ACCEL_TAU
|
|
self.K_A = kalman_params.A
|
|
self.K_C = kalman_params.C
|
|
self.K_K = kalman_params.K
|
|
self.kf = KF1D([[v_lead], [0.0]], self.K_A, self.K_C, self.K_K)
|
|
|
|
def update(self, d_rel: float, y_rel: float, v_rel: float, v_lead: float, measured: float):
|
|
# relative values, copy
|
|
self.dRel = d_rel # LONG_DIST
|
|
self.yRel = y_rel # -LAT_DIST
|
|
self.vRel = v_rel # REL_SPEED
|
|
self.vLead = v_lead
|
|
self.measured = measured # measured or estimate
|
|
|
|
# computed velocity and accelerations
|
|
if self.cnt > 0:
|
|
self.kf.update(self.vLead)
|
|
|
|
self.vLeadK = float(self.kf.x[SPEED][0])
|
|
self.aLeadK = float(self.kf.x[ACCEL][0])
|
|
|
|
# Learn if constant acceleration
|
|
if abs(self.aLeadK) < 0.5:
|
|
self.aLeadTau = _LEAD_ACCEL_TAU
|
|
else:
|
|
self.aLeadTau *= 0.9
|
|
|
|
self.cnt += 1
|
|
|
|
def get_key_for_cluster(self):
|
|
# Weigh y higher since radar is inaccurate in this dimension
|
|
return [self.dRel, self.yRel*2, self.vRel]
|
|
|
|
def reset_a_lead(self, aLeadK: float, aLeadTau: float):
|
|
self.kf = KF1D([[self.vLead], [aLeadK]], self.K_A, self.K_C, self.K_K)
|
|
self.aLeadK = aLeadK
|
|
self.aLeadTau = aLeadTau
|
|
|
|
def get_RadarState(self, model_prob: float = 0.0):
|
|
return {
|
|
"dRel": float(self.dRel),
|
|
"yRel": float(self.yRel),
|
|
"vRel": float(self.vRel),
|
|
"vLead": float(self.vLead),
|
|
"vLeadK": float(self.vLeadK),
|
|
"aLeadK": float(self.aLeadK),
|
|
"aLeadTau": float(self.aLeadTau),
|
|
"status": True,
|
|
"fcw": self.is_potential_fcw(model_prob),
|
|
"modelProb": model_prob,
|
|
"radar": True,
|
|
"radarTrackId": self.identifier,
|
|
}
|
|
|
|
def potential_low_speed_lead(self, v_ego: float):
|
|
# stop for stuff in front of you and low speed, even without model confirmation
|
|
# Radar points closer than 0.75, are almost always glitches on toyota radars
|
|
return abs(self.yRel) < 1.0 and (v_ego < V_EGO_STATIONARY) and (0.75 < self.dRel < 25)
|
|
|
|
def is_potential_fcw(self, model_prob: float):
|
|
return model_prob > .9
|
|
|
|
def __str__(self):
|
|
ret = f"x: {self.dRel:4.1f} y: {self.yRel:4.1f} v: {self.vRel:4.1f} a: {self.aLeadK:4.1f}"
|
|
return ret
|
|
|
|
|
|
def laplacian_pdf(x: float, mu: float, b: float):
|
|
b = max(b, 1e-4)
|
|
return math.exp(-abs(x-mu)/b)
|
|
|
|
|
|
def match_vision_to_track(v_ego: float, lead: capnp._DynamicStructReader, tracks: dict[int, Track]):
|
|
offset_vision_dist = lead.x[0] - RADAR_TO_CAMERA
|
|
|
|
def prob(c):
|
|
prob_d = laplacian_pdf(c.dRel, offset_vision_dist, lead.xStd[0])
|
|
prob_y = laplacian_pdf(c.yRel, -lead.y[0], lead.yStd[0])
|
|
prob_v = laplacian_pdf(c.vRel + v_ego, lead.v[0], lead.vStd[0])
|
|
|
|
# This isn't exactly right, but it's a good heuristic
|
|
return prob_d * prob_y * prob_v
|
|
|
|
track = max(tracks.values(), key=prob)
|
|
|
|
# if no 'sane' match is found return -1
|
|
# stationary radar points can be false positives
|
|
dist_sane = abs(track.dRel - offset_vision_dist) < max([(offset_vision_dist)*.25, 5.0])
|
|
vel_sane = (abs(track.vRel + v_ego - lead.v[0]) < 10) or (v_ego + track.vRel > 3)
|
|
if dist_sane and vel_sane:
|
|
return track
|
|
else:
|
|
return None
|
|
|
|
|
|
def get_RadarState_from_vision(lead_msg: capnp._DynamicStructReader, v_ego: float, model_v_ego: float):
|
|
lead_v_rel_pred = lead_msg.v[0] - model_v_ego
|
|
return {
|
|
"dRel": float(lead_msg.x[0] - RADAR_TO_CAMERA),
|
|
"yRel": float(-lead_msg.y[0]),
|
|
"vRel": float(lead_v_rel_pred),
|
|
"vLead": float(v_ego + lead_v_rel_pred),
|
|
"vLeadK": float(v_ego + lead_v_rel_pred),
|
|
"aLeadK": 0.0,
|
|
"aLeadTau": 0.3,
|
|
"fcw": False,
|
|
"modelProb": float(lead_msg.prob),
|
|
"status": True,
|
|
"radar": False,
|
|
"radarTrackId": -1,
|
|
}
|
|
|
|
|
|
def get_lead(v_ego: float, ready: bool, tracks: dict[int, Track], lead_msg: capnp._DynamicStructReader,
|
|
model_v_ego: float, lead_detection_threshold: float, low_speed_override: bool = True) -> dict[str, Any]:
|
|
# Determine leads, this is where the essential logic happens
|
|
if len(tracks) > 0 and ready and lead_msg.prob > lead_detection_threshold:
|
|
track = match_vision_to_track(v_ego, lead_msg, tracks)
|
|
else:
|
|
track = None
|
|
|
|
lead_dict = {'status': False}
|
|
if track is not None:
|
|
lead_dict = track.get_RadarState(lead_msg.prob)
|
|
elif (track is None) and ready and (lead_msg.prob > lead_detection_threshold):
|
|
lead_dict = get_RadarState_from_vision(lead_msg, v_ego, model_v_ego)
|
|
|
|
if low_speed_override:
|
|
low_speed_tracks = [c for c in tracks.values() if c.potential_low_speed_lead(v_ego)]
|
|
if len(low_speed_tracks) > 0:
|
|
closest_track = min(low_speed_tracks, key=lambda c: c.dRel)
|
|
|
|
# Only choose new track if it is actually closer than the previous one
|
|
if (not lead_dict['status']) or (closest_track.dRel < lead_dict['dRel']):
|
|
lead_dict = closest_track.get_RadarState()
|
|
|
|
return lead_dict
|
|
|
|
|
|
class RadarD:
|
|
def __init__(self, radar_ts: float, delay: int = 0):
|
|
self.points: dict[int, tuple[float, float, float]] = {}
|
|
|
|
self.current_time = 0.0
|
|
|
|
self.tracks: dict[int, Track] = {}
|
|
self.kalman_params = KalmanParams(radar_ts)
|
|
|
|
self.v_ego = 0.0
|
|
self.v_ego_hist = deque([0.0], maxlen=delay+1)
|
|
self.last_v_ego_frame = -1
|
|
|
|
self.radar_state: capnp._DynamicStructBuilder | None = None
|
|
self.radar_state_valid = False
|
|
self.radar_tracks_valid = False
|
|
|
|
self.ready = False
|
|
|
|
# FrogPilot variables
|
|
self.frogpilot_toggles = FrogPilotVariables.toggles
|
|
|
|
self.secret_good_openpilot = self.frogpilot_toggles.secretgoodopenpilot_model
|
|
self.update_toggles = False
|
|
|
|
def update(self, sm: messaging.SubMaster, rr):
|
|
self.ready = sm.seen['modelV2']
|
|
self.current_time = 1e-9*max(sm.logMonoTime.values())
|
|
|
|
radar_points = []
|
|
radar_errors = []
|
|
if rr is not None:
|
|
radar_points = rr.points
|
|
radar_errors = rr.errors
|
|
|
|
if sm.recv_frame['carState'] != self.last_v_ego_frame:
|
|
self.v_ego = sm['carState'].vEgo
|
|
self.v_ego_hist.append(self.v_ego)
|
|
self.last_v_ego_frame = sm.recv_frame['carState']
|
|
|
|
ar_pts = {}
|
|
for pt in radar_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(ids, v_lead, self.kalman_params)
|
|
self.tracks[ids].update(rpt[0], rpt[1], rpt[2], v_lead, rpt[3])
|
|
|
|
# *** publish radarState ***
|
|
self.radar_state_valid = sm.all_checks() and len(radar_errors) == 0
|
|
self.radar_state = log.RadarState.new_message()
|
|
self.radar_state.mdMonoTime = sm.logMonoTime['modelV2']
|
|
self.radar_state.radarErrors = list(radar_errors)
|
|
self.radar_state.carStateMonoTime = sm.logMonoTime['carState']
|
|
|
|
if len(sm['modelV2'].temporalPose.trans) and not self.secret_good_openpilot:
|
|
model_v_ego = sm['modelV2'].temporalPose.trans[0]
|
|
else:
|
|
model_v_ego = self.v_ego
|
|
leads_v3 = sm['modelV2'].leadsV3
|
|
if len(leads_v3) > 1:
|
|
self.radar_state.leadOne = get_lead(self.v_ego, self.ready, self.tracks, leads_v3[0], model_v_ego, self.frogpilot_toggles.lead_detection_threshold, low_speed_override=True)
|
|
self.radar_state.leadTwo = get_lead(self.v_ego, self.ready, self.tracks, leads_v3[1], model_v_ego, self.frogpilot_toggles.lead_detection_threshold, low_speed_override=False)
|
|
|
|
# Update FrogPilot parameters
|
|
if FrogPilotVariables.toggles_updated:
|
|
self.update_toggles = True
|
|
elif self.update_toggles:
|
|
FrogPilotVariables.update_frogpilot_params()
|
|
self.update_toggles = False
|
|
|
|
def publish(self, pm: messaging.PubMaster, lag_ms: float):
|
|
assert self.radar_state is not None
|
|
|
|
radar_msg = messaging.new_message("radarState")
|
|
radar_msg.valid = self.radar_state_valid
|
|
radar_msg.radarState = self.radar_state
|
|
radar_msg.radarState.cumLagMs = lag_ms
|
|
pm.send("radarState", radar_msg)
|
|
|
|
# publish tracks for UI debugging (keep last)
|
|
tracks_msg = messaging.new_message('liveTracks', len(self.tracks))
|
|
tracks_msg.valid = self.radar_state_valid
|
|
for index, tid in enumerate(sorted(self.tracks.keys())):
|
|
tracks_msg.liveTracks[index] = {
|
|
"trackId": tid,
|
|
"dRel": float(self.tracks[tid].dRel),
|
|
"yRel": float(self.tracks[tid].yRel),
|
|
"vRel": float(self.tracks[tid].vRel),
|
|
}
|
|
pm.send('liveTracks', tracks_msg)
|
|
|
|
def update_radardless(self, rr):
|
|
radar_points = []
|
|
radar_errors = []
|
|
if rr is not None:
|
|
radar_points = rr.points
|
|
radar_errors = rr.errors
|
|
|
|
self.radar_tracks_valid = len(radar_errors) == 0
|
|
|
|
self.points = {}
|
|
for pt in radar_points:
|
|
self.points[pt.trackId] = (pt.dRel, pt.yRel, pt.vRel)
|
|
|
|
def publish_radardless(self):
|
|
tracks_msg = messaging.new_message('liveTracks', len(self.points))
|
|
tracks_msg.valid = self.radar_tracks_valid
|
|
for index, tid in enumerate(sorted(self.points.keys())):
|
|
tracks_msg.liveTracks[index] = {
|
|
"trackId": tid,
|
|
"dRel": float(self.points[tid][0]) + RADAR_TO_CAMERA,
|
|
"yRel": -float(self.points[tid][1]),
|
|
"vRel": float(self.points[tid][2]),
|
|
}
|
|
|
|
return tracks_msg
|
|
|
|
# fuses camera and radar data for best lead detection
|
|
def main():
|
|
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")
|
|
with car.CarParams.from_bytes(Params().get("CarParams", block=True)) as msg:
|
|
CP = msg
|
|
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
|
|
can_sock = messaging.sub_sock('can')
|
|
pub_sock = messaging.pub_sock('liveTracks')
|
|
|
|
RI = RadarInterface(CP)
|
|
|
|
# TODO timing is different between cars, need a single time step for all cars
|
|
# TODO just take the fastest one for now, and keep resending same messages for slower radars
|
|
rk = Ratekeeper(1.0 / CP.radarTimeStep, print_delay_threshold=None)
|
|
RD = RadarD(CP.radarTimeStep, RI.delay)
|
|
|
|
if not FrogPilotVariables.toggles.radarless_model:
|
|
sm = messaging.SubMaster(['modelV2', 'carState'], frequency=int(1./DT_CTRL))
|
|
pm = messaging.PubMaster(['radarState', 'liveTracks'])
|
|
|
|
while True:
|
|
can_strings = messaging.drain_sock_raw(can_sock, wait_for_one=True)
|
|
rr = RI.update(can_strings)
|
|
sm.update(0)
|
|
if rr is None:
|
|
continue
|
|
|
|
RD.update(sm, rr)
|
|
RD.publish(pm, -rk.remaining*1000.0)
|
|
rk.monitor_time()
|
|
else:
|
|
while True:
|
|
can_strings = messaging.drain_sock_raw(can_sock, wait_for_one=True)
|
|
rr = RI.update(can_strings)
|
|
if rr is None:
|
|
continue
|
|
|
|
RD.update_radardless(rr)
|
|
msg = RD.publish_radardless()
|
|
pub_sock.send(msg.to_bytes())
|
|
|
|
rk.monitor_time()
|
|
|
|
if __name__ == "__main__":
|
|
main()
|