From f01357d02515bfb596ada5e131c0ce40e935b59c Mon Sep 17 00:00:00 2001 From: infiniteCable <75014343+infiniteCable@users.noreply.github.com> Date: Sat, 19 Apr 2025 15:29:25 +0200 Subject: [PATCH] Update radard.py revert --- selfdrive/controls/radard.py | 96 ++++++++++-------------------------- 1 file changed, 25 insertions(+), 71 deletions(-) diff --git a/selfdrive/controls/radard.py b/selfdrive/controls/radard.py index 92efbde46..bee424405 100755 --- a/selfdrive/controls/radard.py +++ b/selfdrive/controls/radard.py @@ -1,5 +1,5 @@ #!/usr/bin/env python3 -import math, time +import math import numpy as np from collections import deque from typing import Any @@ -18,16 +18,7 @@ from opendbc.sunnypilot.car.hyundai.values import HyundaiFlagsSP # Default lead acceleration decay set to 50% at 1s -_LEAD_ACCEL_TAU = 1.0 - -# Exponential decay / growth factors -TAU_GROW = 1.05 # pro Update inkrementell grösser -TAU_SHRINK= 0.90 # wenn |a| klein ⇒ schneller kleiner -TAU_MIN = 0.4 - -# Vision‑blending -BLEND_KF = 0.2 # Anteil des vorgefilterten aLeadK -BLEND_VREL_DERIV = 0.3 # Anteil Δd/Δt in vRel +_LEAD_ACCEL_TAU = 1.5 # radar tracks SPEED, ACCEL = 0, 1 # Kalman filter states enum @@ -65,13 +56,11 @@ 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.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) - self.last_dRel = None # für derivative vRel aus Vision - self.last_t = None def update(self, d_rel: float, y_rel: float, v_rel: float, v_lead: float, measured: float): # relative values, copy @@ -89,15 +78,13 @@ class Track: self.aLeadK = float(self.kf.x[ACCEL][0]) # Learn if constant acceleration - # adaptive aLeadTau if abs(self.aLeadK) < 0.5: - self.aLeadTau = min(max(self.aLeadTau, 0.05) * TAU_GROW, _LEAD_ACCEL_TAU) + self.aLeadTau.x = _LEAD_ACCEL_TAU else: - self.aLeadTau = max(self.aLeadTau * TAU_SHRINK, TAU_MIN) + self.aLeadTau.update(0.0) self.cnt += 1 - def get_RadarState(self, model_prob: float = 0.0): return { "dRel": float(self.dRel), @@ -106,7 +93,7 @@ class Track: "vLead": float(self.vLead), "vLeadK": float(self.vLeadK), "aLeadK": float(self.aLeadK), - "aLeadTau": float(self.aLeadTau), + "aLeadTau": float(self.aLeadTau.x), "status": True, "fcw": self.is_potential_fcw(model_prob), "modelProb": model_prob, @@ -156,78 +143,45 @@ def match_vision_to_track(v_ego: float, lead: capnp._DynamicStructReader, tracks def get_RadarState_from_vision(lead_msg: capnp._DynamicStructReader, v_ego: float, model_v_ego: float): - # Persistent states - prev_ts = getattr(get_RadarState_from_vision, "prev_ts", time.monotonic()) - prev_aLeadK = getattr(get_RadarState_from_vision, "prev_aLeadK", 0.0) - last_d = getattr(get_RadarState_from_vision, "last_d", None) - - # Timing - now = time.monotonic() - dt = now - prev_ts if now > prev_ts else 0.0 - get_RadarState_from_vision.prev_ts = now - - # Baseline model values - d_rel = lead_msg.x[0] - RADAR_TO_CAMERA - v_rel_mod = lead_msg.v[0] - model_v_ego - a_mod = lead_msg.a[0] if len(lead_msg.a) else 0.0 - - # Derivative‑based vRel - v_rel_der = None - if dt > 1e-3 and last_d is not None: - v_rel_der = (d_rel - last_d) / dt - get_RadarState_from_vision.last_d = d_rel - - # Blend derivative with model (saturate if None) - v_rel_pred = (v_rel_mod if v_rel_der is None else - (1.0 - BLEND_VREL_DERIV) * v_rel_mod + BLEND_VREL_DERIV * v_rel_der) - - # aLeadK blending / smoothing - aLeadK_blend = (1.0 - BLEND_KF) * a_mod + BLEND_KF * prev_aLeadK - get_RadarState_from_vision.prev_aLeadK = aLeadK_blend - + lead_v_rel_pred = lead_msg.v[0] - model_v_ego return { - "dRel": float(d_rel), - "yRel": float(-lead_msg.y[0]), - "vRel": float(v_rel_pred), - "vLead": float(v_ego + v_rel_pred), - "vLeadK": float(v_ego + v_rel_pred), - "aLeadK": float(aLeadK_blend), + "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": float(lead_msg.a[0]), "aLeadTau": 0.3, - "fcw": False, + "fcw": False, "modelProb": float(lead_msg.prob), - "status": True, - "radar": False, + "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, CP: structs.CarParams, CP_SP: structs.CarParamsSP, low_speed_override: bool = True) -> dict[str, Any]: - track: Track | None = None - # Determine leads, this is where the essential logic happens - if len(tracks) > 0 and ready and lead_msg.prob > 0.5: + if len(tracks) > 0 and ready and lead_msg.prob > .5: 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) lead_dict = get_custom_yrel(CP, CP_SP, lead_dict, lead_msg) - elif ready and lead_msg.prob > 0.5: + elif (track is None) and ready and (lead_msg.prob > .5): lead_dict = get_RadarState_from_vision(lead_msg, v_ego, model_v_ego) - else: - lead_dict = {"status": False} - - if track is not None and lead_msg.prob > 0.5 and lead_dict["status"]: - d_vision = lead_msg.x[0] - RADAR_TO_CAMERA - if abs(track.dRel - d_vision) > 3.0: # Threshold 3 m - lead_dict = track.get_RadarState(lead_msg.prob) - lead_dict = get_custom_yrel(CP, CP_SP, lead_dict, lead_msg) if low_speed_override: low_speed_tracks = [c for c in tracks.values() if c.potential_low_speed_lead(v_ego)] - if low_speed_tracks: + if len(low_speed_tracks) > 0: closest_track = min(low_speed_tracks, key=lambda c: c.dRel) - if (not lead_dict["status"]) or (closest_track.dRel < lead_dict["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