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https://github.com/firestar5683/StarPilot.git
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firestar5683
412 lines
17 KiB
Python
412 lines
17 KiB
Python
#!/usr/bin/env python3
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import math
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import numpy as np
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from collections import deque
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from types import SimpleNamespace
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from typing import Any
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import capnp
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from cereal import messaging, log, car, custom
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from openpilot.common.filter_simple import FirstOrderFilter
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from openpilot.common.params import Params
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from openpilot.common.realtime import DT_MDL, Priority, config_realtime_process
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from openpilot.common.swaglog import cloudlog
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from openpilot.common.simple_kalman import KF1D
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from openpilot.selfdrive.controls.lib.desire_helper import LaneChangeDirection, LaneChangeState
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from openpilot.starpilot.common.starpilot_variables import get_starpilot_toggles
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# Default lead acceleration decay set to 50% at 1s
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_LEAD_ACCEL_TAU = 0.6
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# radar tracks
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SPEED, ACCEL = 0, 1 # Kalman filter states enum
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# stationary qualification parameters
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V_EGO_STATIONARY = 4. # no stationary object flag below this speed
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RADAR_TO_CENTER = 2.7 # (deprecated) RADAR is ~ 2.7m ahead from center of car
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RADAR_TO_CAMERA = 1.52 # RADAR is ~ 1.5m ahead from center of mesh frame
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G90_RADAR_LOW_SPEED_MAX_DIST = 12.0
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G90_RADAR_LOW_SPEED_MAX_Y = 0.6
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class KalmanParams:
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def __init__(self, dt: float):
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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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dts = [i * 0.01 for i 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 = [[np.interp(dt, dts, K0)], [np.interp(dt, dts, K1)]]
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class Track:
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def __init__(self, identifier: int, v_lead: float, kalman_params: KalmanParams):
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self.identifier = identifier
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self.cnt = 0
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self.aLeadTau = FirstOrderFilter(_LEAD_ACCEL_TAU, 0.45, DT_MDL)
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self.K_A = kalman_params.A
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self.K_C = kalman_params.C
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self.K_K = kalman_params.K
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self.kf = KF1D([[v_lead], [0.0]], self.K_A, self.K_C, self.K_K)
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self.leadTrackID = 0
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def update(self, d_rel: float, y_rel: float, v_rel: float, v_lead: float, measured: float):
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# relative values, copy
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self.dRel = d_rel # LONG_DIST
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self.yRel = y_rel # -LAT_DIST
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self.vRel = v_rel # REL_SPEED
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self.vLead = v_lead
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self.measured = measured # measured or estimate
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# computed velocity and accelerations
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if self.cnt > 0:
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self.kf.update(self.vLead)
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self.vLeadK = float(self.kf.x[SPEED][0])
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self.aLeadK = float(self.kf.x[ACCEL][0])
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# Learn if constant acceleration
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if abs(self.aLeadK) < 0.5:
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self.aLeadTau.x = min(max(self.aLeadTau.x, 1e-2) * 1.1, _LEAD_ACCEL_TAU)
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else:
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self.aLeadTau.update(0.0)
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self.cnt += 1
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def get_RadarState(self, model_prob: float = 0.0):
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return {
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"dRel": float(self.dRel),
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"yRel": float(self.yRel),
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"vRel": float(self.vRel),
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"vLead": float(self.vLead),
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"vLeadK": float(self.vLeadK),
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"aLeadK": float(self.aLeadK),
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"aLeadTau": float(self.aLeadTau.x),
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"status": True,
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"fcw": self.is_potential_fcw(model_prob),
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"modelProb": model_prob,
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"radar": True,
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"radarTrackId": self.identifier,
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}
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def potential_adjacent_lead(self, left: bool, standstill: bool, model_data: capnp._DynamicStructReader):
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if standstill or self.vLead < 1 or self.leadTrackID == self.identifier:
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return False
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if left:
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left_lane = np.interp(self.dRel, model_data.laneLines[1].x, model_data.laneLines[1].y)
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return -self.yRel < left_lane
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right_lane = np.interp(self.dRel, model_data.laneLines[2].x, model_data.laneLines[2].y)
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return -self.yRel > right_lane
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def potential_low_speed_lead(self, v_ego: float):
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# stop for stuff in front of you and low speed, even without model confirmation
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# Radar points closer than 0.75, are almost always glitches on toyota radars
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return abs(self.yRel) < 1.0 and (v_ego < V_EGO_STATIONARY) and (0.75 < self.dRel < 25)
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def is_potential_fcw(self, model_prob: float):
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return model_prob > .9
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def __str__(self):
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ret = f"x: {self.dRel:4.1f} y: {self.yRel:4.1f} v: {self.vRel:4.1f} a: {self.aLeadK:4.1f}"
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return ret
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def laplacian_pdf(x: float, mu: float, b: float):
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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 g90_radar_lead_lateral_sane(track: Track) -> bool:
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# The G90 extended radar channels can report close side ghosts in tight turns.
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# Keep the gate tight at close range, then widen gradually with distance.
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max_y = min(6.0, 1.5 + 0.08 * max(track.dRel, 0.0))
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return abs(track.yRel) <= max_y
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def g90_low_speed_radar_lead_sane(track: Track, v_ego: float) -> bool:
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return (track.cnt >= 3 and v_ego < 3.0 and
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0.75 < track.dRel < G90_RADAR_LOW_SPEED_MAX_DIST and
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abs(track.yRel) < G90_RADAR_LOW_SPEED_MAX_Y)
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def track_matches_vision(track: Track, lead: capnp._DynamicStructReader, v_ego: float, *,
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dist_scale: float, dist_floor: float, vel_limit: float,
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y_std_scale: float, y_floor: float) -> bool:
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offset_vision_dist = lead.x[0] - RADAR_TO_CAMERA
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dist_sane = abs(track.dRel - offset_vision_dist) < max(abs(offset_vision_dist) * dist_scale, dist_floor)
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vel_sane = (abs(track.vRel + v_ego - lead.v[0]) < vel_limit) or (v_ego + track.vRel > 3)
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lat_sane = abs(track.yRel + lead.y[0]) < max(y_floor, y_std_scale * max(float(lead.yStd[0]), 0.2))
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return dist_sane and vel_sane and lat_sane
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def match_vision_to_track(v_ego: float, lead: capnp._DynamicStructReader, model_data: capnp._DynamicStructReader, tracks: dict[int, Track],
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starpilot_toggles: SimpleNamespace, g90_radar_filter: bool = False,
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preferred_track_id: int = -1):
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if model_data.meta.laneChangeState == LaneChangeState.laneChangeStarting and getattr(starpilot_toggles, "human_lane_changes", False):
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direction = model_data.meta.laneChangeDirection
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if direction == LaneChangeDirection.left:
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tracks = {k: v for k, v in tracks.items() if v.yRel > 0}
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elif direction == LaneChangeDirection.right:
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tracks = {k: v for k, v in tracks.items() if v.yRel < 0}
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if g90_radar_filter:
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tracks = {k: v for k, v in tracks.items() if g90_radar_lead_lateral_sane(v)}
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if not tracks:
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return None
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def prob(c):
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offset_vision_dist = lead.x[0] - RADAR_TO_CAMERA
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prob_d = laplacian_pdf(c.dRel, offset_vision_dist, lead.xStd[0])
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prob_y = laplacian_pdf(c.yRel, -lead.y[0], lead.yStd[0])
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prob_v = laplacian_pdf(c.vRel + v_ego, lead.v[0], lead.vStd[0])
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return prob_d * prob_y * prob_v
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track = max(tracks.values(), 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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if track_matches_vision(track, lead, v_ego,
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dist_scale=0.25, dist_floor=5.0,
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vel_limit=10.0, y_std_scale=1.0, y_floor=1.0):
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return track
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# Some vehicles intermittently drop a good radar match on large leads (semis are
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# a common offender). If the same track is still present and only missed the
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# strict vision gate by a small margin, keep the previous radar match instead of
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# oscillating between radar and vision estimates.
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preferred_track = tracks.get(preferred_track_id)
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if preferred_track is not None and preferred_track.cnt >= 3:
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if track_matches_vision(preferred_track, lead, v_ego,
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dist_scale=0.40, dist_floor=8.0,
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vel_limit=13.0, y_std_scale=2.0, y_floor=1.5):
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return preferred_track
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return None
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def get_RadarState_from_vision(lead_msg: capnp._DynamicStructReader, v_ego: float, model_v_ego: float, model_prob: float):
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prev_aLeadK = getattr(get_RadarState_from_vision, "prev_aLeadK", 0.0)
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blended_aLeadK = 0.8 * float(lead_msg.a[0]) + 0.2 * prev_aLeadK
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get_RadarState_from_vision.prev_aLeadK = blended_aLeadK
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return {
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"dRel": float(lead_msg.x[0] - RADAR_TO_CAMERA),
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"yRel": float(-lead_msg.y[0]),
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"vRel": float(lead_msg.v[0] - model_v_ego),
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"vLead": float(v_ego + (lead_msg.v[0] - model_v_ego)),
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"vLeadK": float(v_ego + (lead_msg.v[0] - model_v_ego)),
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"aLeadK": blended_aLeadK,
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"aLeadTau": 0.3,
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"fcw": False,
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"modelProb": float(model_prob),
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"status": True,
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"radar": False,
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"radarTrackId": -1,
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}
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def get_lead(v_ego: float, ready: bool, tracks: dict[int, Track], lead_msg: capnp._DynamicStructReader,
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model_v_ego: float, model_data: capnp._DynamicStructReader, standstill: bool,
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starpilot_plan: capnp._DynamicStructReader, starpilot_toggles: SimpleNamespace,
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low_speed_override: bool = True, g90_radar_filter: bool = False, lead_prob: float | None = None,
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preferred_track_id: int = -1) -> dict[str, Any]:
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lead_detection_probability = float(getattr(starpilot_toggles, "lead_detection_probability", 0.35))
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filtered_lead_prob = float(lead_msg.prob if lead_prob is None else lead_prob)
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# Determine leads, this is where the essential logic happens
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if len(tracks) > 0 and ready and filtered_lead_prob > lead_detection_probability:
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track = match_vision_to_track(v_ego, lead_msg, model_data, tracks, starpilot_toggles, g90_radar_filter,
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preferred_track_id=preferred_track_id)
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else:
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track = None
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lead_dict = {'status': False}
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if track is not None:
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lead_dict = track.get_RadarState(filtered_lead_prob)
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elif (track is None) and ready and (filtered_lead_prob > lead_detection_probability):
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lead_dict = get_RadarState_from_vision(lead_msg, v_ego, model_v_ego, filtered_lead_prob)
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if low_speed_override:
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if g90_radar_filter:
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low_speed_tracks = [c for c in tracks.values() if g90_low_speed_radar_lead_sane(c, v_ego)]
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else:
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low_speed_tracks = [c for c in tracks.values() if c.potential_low_speed_lead(v_ego)]
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if len(low_speed_tracks) > 0:
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closest_track = min(low_speed_tracks, key=lambda c: c.dRel)
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# Only choose new track if it is actually closer than the previous one
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if (not lead_dict['status']) or (closest_track.dRel < lead_dict['dRel']):
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lead_dict = closest_track.get_RadarState()
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for track in tracks.values():
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track.leadTrackID = lead_dict.get('radarTrackId', -1)
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if 'dRel' in lead_dict:
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lead_dict['dRel'] -= starpilot_plan.increasedStoppedDistance
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return lead_dict
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def get_adjacent_lead(tracks: dict[int, Track], standstill: bool, model_data: capnp._DynamicStructReader, left: bool = True) -> dict[str, Any]:
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lead_dict = {'status': False}
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adjacent_tracks = [c for c in tracks.values() if c.potential_adjacent_lead(left, standstill, model_data)]
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if len(adjacent_tracks) > 0:
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closest_track = min(adjacent_tracks, key=lambda c: c.dRel)
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lead_dict = closest_track.get_RadarState()
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return lead_dict
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class RadarD:
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def __init__(self, radar_ts: float = DT_MDL, delay: float = 0.0, g90_radar_filter: bool = False):
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self.current_time = 0.0
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self.tracks: dict[int, Track] = {}
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self.kalman_params = KalmanParams(radar_ts)
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self.g90_radar_filter = g90_radar_filter
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self.lead_prob_filters = [FirstOrderFilter(0.0, 0.2, radar_ts) for _ in range(2)]
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self.prev_lead_track_ids = [-1, -1]
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self.v_ego = 0.0
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self.v_ego_hist = deque([0.0], maxlen=int(round(delay / DT_MDL)) + 1)
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self.last_v_ego_frame = -1
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self.radar_state: capnp._DynamicStructBuilder | None = None
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self.radar_state_valid = False
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self.ready = False
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self.starpilot_radar_state = custom.StarPilotRadarState.new_message()
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self.starpilot_toggles = get_starpilot_toggles()
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def update(self, sm: messaging.SubMaster, rr: car.RadarData):
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self.ready = sm.seen['modelV2']
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self.current_time = 1e-9 * max(sm.logMonoTime.values())
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if sm.recv_frame['carState'] != self.last_v_ego_frame:
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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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self.last_v_ego_frame = sm.recv_frame['carState']
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ar_pts = {pt.trackId: [pt.dRel, pt.yRel, pt.vRel, pt.measured] for pt in rr.points}
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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, rpt in ar_pts.items():
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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(ids, 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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# *** publish radarState ***
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self.radar_state_valid = sm.all_checks()
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self.radar_state = log.RadarState.new_message()
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self.radar_state.mdMonoTime = sm.logMonoTime['modelV2']
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self.radar_state.radarErrors = rr.errors
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self.radar_state.carStateMonoTime = sm.logMonoTime['carState']
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self.starpilot_radar_state = custom.StarPilotRadarState.new_message()
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if len(sm['modelV2'].velocity.x):
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model_v_ego = sm['modelV2'].velocity.x[0]
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else:
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model_v_ego = self.v_ego
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leads_v3 = sm['modelV2'].leadsV3
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if len(leads_v3) > 1:
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for i in range(2):
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lead_prob = float(leads_v3[i].prob)
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if lead_prob > self.lead_prob_filters[i].x:
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self.lead_prob_filters[i].x = lead_prob
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else:
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self.lead_prob_filters[i].update(lead_prob)
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self.radar_state.leadOne = get_lead(self.v_ego, self.ready, self.tracks, leads_v3[0], model_v_ego, sm['modelV2'],
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sm['carState'].standstill, sm['starpilotPlan'], self.starpilot_toggles, low_speed_override=True,
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g90_radar_filter=self.g90_radar_filter, lead_prob=self.lead_prob_filters[0].x,
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preferred_track_id=self.prev_lead_track_ids[0])
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self.radar_state.leadTwo = get_lead(self.v_ego, self.ready, self.tracks, leads_v3[1], model_v_ego, sm['modelV2'],
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sm['carState'].standstill, sm['starpilotPlan'], self.starpilot_toggles, low_speed_override=False,
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g90_radar_filter=self.g90_radar_filter, lead_prob=self.lead_prob_filters[1].x,
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preferred_track_id=self.prev_lead_track_ids[1])
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for i, lead in enumerate((self.radar_state.leadOne, self.radar_state.leadTwo)):
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if lead.status and getattr(lead, "radar", False):
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self.prev_lead_track_ids[i] = int(getattr(lead, "radarTrackId", -1))
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elif (not lead.status) or (self.prev_lead_track_ids[i] not in self.tracks):
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self.prev_lead_track_ids[i] = -1
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if self.ready and (self.starpilot_toggles.adjacent_lead_tracking or self.starpilot_toggles.human_lane_changes):
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self.starpilot_radar_state.leadLeft = get_adjacent_lead(self.tracks, sm['carState'].standstill, sm['modelV2'], left=True)
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self.starpilot_radar_state.leadRight = get_adjacent_lead(self.tracks, sm['carState'].standstill, sm['modelV2'], left=False)
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self.starpilot_toggles = get_starpilot_toggles(sm)
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def publish(self, pm: messaging.PubMaster):
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assert self.radar_state is not None
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radar_msg = messaging.new_message("radarState")
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radar_msg.valid = self.radar_state_valid
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radar_msg.radarState = self.radar_state
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pm.send("radarState", radar_msg)
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starpilot_radar_msg = messaging.new_message("starpilotRadarState")
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starpilot_radar_msg.valid = self.radar_state_valid
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starpilot_radar_msg.starpilotRadarState = self.starpilot_radar_state
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pm.send("starpilotRadarState", starpilot_radar_msg)
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# fuses camera and radar data for best lead detection
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def main() -> None:
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config_realtime_process(5, Priority.CTRL_LOW)
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|
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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 = messaging.log_from_bytes(Params().get("CarParams", block=True), car.CarParams)
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cloudlog.info("radard got CarParams")
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|
|
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# *** setup messaging
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sm = messaging.SubMaster(['modelV2', 'carState', 'liveTracks'], poll='modelV2',
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ignore_valid=['starpilotPlan'])
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pm = messaging.PubMaster(['radarState'])
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|
|
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radar_ts = float(getattr(CP, "radarTimeStepDEPRECATED", DT_MDL) or DT_MDL)
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if not 0.01 < radar_ts < 0.2:
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|
radar_ts = DT_MDL
|
|
|
|
g90_radar_filter = CP.brand == "hyundai" and CP.carFingerprint == "GENESIS_G90"
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RD = RadarD(radar_ts=radar_ts, delay=CP.radarDelay, g90_radar_filter=g90_radar_filter)
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|
|
|
sm = sm.extend(['starpilotPlan'])
|
|
pm = pm.extend(['starpilotRadarState'])
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|
|
|
while 1:
|
|
sm.update()
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|
|
|
RD.update(sm, sm['liveTracks'])
|
|
RD.publish(pm)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|