firestar5683
391 lines
15 KiB
Python
391 lines
15 KiB
Python
#!/usr/bin/env python3
|
|
import math
|
|
import numpy as np
|
|
from collections import deque
|
|
from types import SimpleNamespace
|
|
from typing import Any
|
|
|
|
import capnp
|
|
from cereal import messaging, log, car, custom
|
|
from openpilot.common.filter_simple import FirstOrderFilter
|
|
from openpilot.common.params import Params
|
|
from openpilot.common.realtime import DT_MDL, Priority, config_realtime_process
|
|
from openpilot.common.swaglog import cloudlog
|
|
from openpilot.common.simple_kalman import KF1D
|
|
from openpilot.selfdrive.controls.lib.desire_helper import LaneChangeDirection, LaneChangeState
|
|
|
|
from openpilot.frogpilot.common.frogpilot_variables import THRESHOLD, get_frogpilot_toggles
|
|
|
|
|
|
# Default lead acceleration decay set to 50% at 1s
|
|
_LEAD_ACCEL_TAU = 0.6
|
|
|
|
# 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 = [i * 0.01 for i 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 = [[np.interp(dt, dts, K0)], [np.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 = FirstOrderFilter(_LEAD_ACCEL_TAU, 0.45, DT_MDL)
|
|
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)
|
|
|
|
# FrogPilot variables
|
|
self.leadLeft = False
|
|
self.leadRight = False
|
|
|
|
self.leadTrackID = 0
|
|
|
|
self.radarfulFilter = FirstOrderFilter(0, 0.5, self.K_A[0][1])
|
|
|
|
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.x = min(max(self.aLeadTau.x, 1e-2) * 1.1, _LEAD_ACCEL_TAU)
|
|
else:
|
|
self.aLeadTau.update(0.0)
|
|
|
|
self.cnt += 1
|
|
|
|
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.x),
|
|
"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
|
|
|
|
# FrogPilot variables
|
|
def potential_adjacent_lead(self, left: bool, model_data: capnp._DynamicStructReader):
|
|
if self.vLeadK < 1 or self.leadTrackID == self.identifier:
|
|
return False
|
|
|
|
far_left_lane = np.interp(self.dRel, model_data.laneLines[0].x, model_data.laneLines[0].y)
|
|
left_lane = np.interp(self.dRel, model_data.laneLines[1].x, model_data.laneLines[1].y)
|
|
right_lane = np.interp(self.dRel, model_data.laneLines[2].x, model_data.laneLines[2].y)
|
|
far_right_lane = np.interp(self.dRel, model_data.laneLines[3].x, model_data.laneLines[3].y)
|
|
|
|
self.leadLeft = far_left_lane < -self.yRel < left_lane and self.dRel < model_data.position.x[-1]
|
|
self.leadRight = right_lane < -self.yRel < far_right_lane and self.dRel < model_data.position.x[-1]
|
|
|
|
if left:
|
|
return self.leadLeft
|
|
else:
|
|
return self.leadRight
|
|
|
|
def potential_far_lead(self, standstill: bool, model_data: capnp._DynamicStructReader):
|
|
if standstill or self.vLead < 1 or abs(self.yRel) > 1:
|
|
return False
|
|
|
|
left_lane = np.interp(self.dRel, model_data.laneLines[1].x, model_data.laneLines[1].y)
|
|
right_lane = np.interp(self.dRel, model_data.laneLines[2].x, model_data.laneLines[2].y)
|
|
|
|
if left_lane < -self.yRel < right_lane:
|
|
self.radarfulFilter.update(1)
|
|
return True
|
|
else:
|
|
self.radarfulFilter.update(0)
|
|
return False
|
|
|
|
|
|
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, model_data: capnp._DynamicStructReader, tracks: dict[int, Track], frogpilot_toggles: SimpleNamespace):
|
|
# FrogPilot variables
|
|
if model_data.meta.laneChangeState == LaneChangeState.laneChangeStarting and frogpilot_toggles.human_lane_changes:
|
|
direction = model_data.meta.laneChangeDirection
|
|
|
|
if direction == LaneChangeDirection.left:
|
|
left_tracks = [track for track in tracks.values() if track.leadLeft]
|
|
if left_tracks:
|
|
return min(left_tracks, key=lambda c: c.dRel)
|
|
|
|
elif direction == LaneChangeDirection.right:
|
|
right_tracks = [track for track in tracks.values() if track.leadRight]
|
|
if right_tracks:
|
|
return min(right_tracks, key=lambda c: c.dRel)
|
|
|
|
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):
|
|
prev_aLeadK = getattr(get_RadarState_from_vision, "prev_aLeadK", 0.0)
|
|
blended_aLeadK = 0.8 * float(lead_msg.a[0]) + 0.2 * prev_aLeadK
|
|
get_RadarState_from_vision.prev_aLeadK = blended_aLeadK
|
|
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": blended_aLeadK,
|
|
"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, model_data: capnp._DynamicStructReader, standstill: bool,
|
|
frogpilot_plan: capnp._DynamicStructReader, frogpilot_toggles: SimpleNamespace,
|
|
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 > frogpilot_toggles.lead_detection_probability:
|
|
track = match_vision_to_track(v_ego, lead_msg, model_data, tracks, frogpilot_toggles)
|
|
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 > frogpilot_toggles.lead_detection_probability):
|
|
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()
|
|
|
|
if low_speed_override and not lead_dict['status'] and len(tracks) > 0:
|
|
far_lead_tracks = [c for c in tracks.values() if c.potential_far_lead(standstill, model_data) and c.radarfulFilter.x >= THRESHOLD]
|
|
if len(far_lead_tracks) > 0:
|
|
closest_track = min(far_lead_tracks, key=lambda c: c.dRel)
|
|
lead_dict = closest_track.get_RadarState()
|
|
|
|
# FrogPilot variables
|
|
for track in tracks.values():
|
|
track.leadTrackID = lead_dict.get('radarTrackId', -1)
|
|
|
|
if 'dRel' in lead_dict:
|
|
lead_dict['dRel'] -= frogpilot_plan.increasedStoppedDistance
|
|
|
|
return lead_dict
|
|
|
|
|
|
# FrogPilot variables
|
|
def get_adjacent_lead(tracks: dict[int, Track], model_data: capnp._DynamicStructReader, left: bool = True) -> dict[str, Any]:
|
|
lead_dict = {'status': False}
|
|
|
|
adjacent_tracks = [c for c in tracks.values() if c.potential_adjacent_lead(left, model_data)]
|
|
if len(adjacent_tracks) > 0:
|
|
closest_track = min(adjacent_tracks, key=lambda c: c.dRel)
|
|
lead_dict = closest_track.get_RadarState()
|
|
|
|
return lead_dict
|
|
|
|
|
|
class RadarD:
|
|
def __init__(self, delay: float = 0.0):
|
|
self.current_time = 0.0
|
|
|
|
self.tracks: dict[int, Track] = {}
|
|
self.kalman_params = KalmanParams(DT_MDL)
|
|
|
|
self.v_ego = 0.0
|
|
self.v_ego_hist = deque([0.0], maxlen=int(round(delay / DT_MDL))+1)
|
|
self.last_v_ego_frame = -1
|
|
|
|
self.radar_state: capnp._DynamicStructBuilder | None = None
|
|
self.radar_state_valid = False
|
|
|
|
self.ready = False
|
|
|
|
# FrogPilot variables
|
|
self.frogpilot_radar_state = custom.FrogPilotRadarState.new_message()
|
|
|
|
self.frogpilot_toggles = get_frogpilot_toggles()
|
|
|
|
def update(self, sm: messaging.SubMaster, rr: car.RadarData):
|
|
self.ready = sm.seen['modelV2']
|
|
self.current_time = 1e-9*max(sm.logMonoTime.values())
|
|
|
|
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 = {pt.trackId: [pt.dRel, pt.yRel, pt.vRel, pt.measured] for pt in rr.points}
|
|
|
|
# *** 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()
|
|
self.radar_state = log.RadarState.new_message()
|
|
self.radar_state.mdMonoTime = sm.logMonoTime['modelV2']
|
|
self.radar_state.radarErrors = rr.errors
|
|
self.radar_state.carStateMonoTime = sm.logMonoTime['carState']
|
|
|
|
if len(sm['modelV2'].velocity.x):
|
|
model_v_ego = sm['modelV2'].velocity.x[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, sm['modelV2'],
|
|
sm['carState'].standstill, sm['frogpilotPlan'], self.frogpilot_toggles, low_speed_override=True)
|
|
self.radar_state.leadTwo = get_lead(self.v_ego, self.ready, self.tracks, leads_v3[1], model_v_ego, sm['modelV2'],
|
|
sm['carState'].standstill, sm['frogpilotPlan'], self.frogpilot_toggles, low_speed_override=False)
|
|
|
|
# FrogPilot variables
|
|
if self.ready and (self.frogpilot_toggles.adjacent_lead_tracking or self.frogpilot_toggles.human_lane_changes):
|
|
self.frogpilot_radar_state.leadLeft = get_adjacent_lead(self.tracks, sm['modelV2'], left=True)
|
|
self.frogpilot_radar_state.leadRight = get_adjacent_lead(self.tracks, sm['modelV2'], left=False)
|
|
|
|
self.frogpilot_toggles = get_frogpilot_toggles(sm)
|
|
|
|
def publish(self, pm: messaging.PubMaster):
|
|
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
|
|
pm.send("radarState", radar_msg)
|
|
|
|
# FrogPilot variables
|
|
frogpilot_radar_msg = messaging.new_message("frogpilotRadarState")
|
|
frogpilot_radar_msg.valid = self.radar_state_valid
|
|
frogpilot_radar_msg.frogpilotRadarState = self.frogpilot_radar_state
|
|
pm.send("frogpilotRadarState", frogpilot_radar_msg)
|
|
|
|
|
|
# fuses camera and radar data for best lead detection
|
|
def main() -> 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 = messaging.log_from_bytes(Params().get("CarParams", block=True), car.CarParams)
|
|
cloudlog.info("radard got CarParams")
|
|
|
|
# *** setup messaging
|
|
sm = messaging.SubMaster(['modelV2', 'carState', 'liveTracks'], poll='modelV2',
|
|
ignore_valid=['frogpilotPlan'])
|
|
pm = messaging.PubMaster(['radarState'])
|
|
|
|
RD = RadarD(CP.radarDelay)
|
|
|
|
# FrogPilot variables
|
|
sm = sm.extend(['frogpilotPlan'])
|
|
pm = pm.extend(['frogpilotRadarState'])
|
|
|
|
while 1:
|
|
sm.update()
|
|
|
|
RD.update(sm, sm['liveTracks'])
|
|
RD.publish(pm)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|