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5d10875 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 | """FailureDetectionAgent — layer 1.
Scans candidate-vs-ground-truth predictions on the synthetic populations
(bevfusion scene campaign + vectorized rate population) for:
* classification flips (pedestrian->construction_cone, pedestrian->vehicle,
bicycle->vehicle, ..., anything->background),
* localization regressions (mean center error delta),
* detection regressions (per-class recall delta),
* confidence-calibration regressions (mean confidence on correct
predictions),
* temporal regressions (per-track prediction churn).
Every emitted FailureEvent carries its deterministic detection basis (counts,
denominators, metric deltas) — the agent flags, it does not judge.
"""
from __future__ import annotations
from collections import defaultdict
from typing import Any, Dict, List, Tuple
import numpy as np
from sensorflow.agentic import data as data_mod
from sensorflow.agentic.agents.base import BaseAgent, compact, no_escalation
from sensorflow.agentic.models import (AgentEscalation, DetectionBasis,
FailureEvent, FailureInstance, new_id)
SAFETY_CRITICAL = {"pedestrian", "cyclist", "motorcycle"}
LOCALIZATION_DELTA_THRESHOLD_M = 0.02
CONFIDENCE_DELTA_THRESHOLD = 0.03
CHURN_DELTA_THRESHOLD = 0.02
class FailureDetectionAgent(BaseAgent):
name = "failure_detection"
version = "1.0.0"
epistemic_status = "DERIVED"
def detect(self, seed: int = data_mod.DEFAULT_SEED) -> Tuple[List[FailureEvent], Dict]:
"""Full campaign scan. Returns (events, scan_summary)."""
campaign = data_mod.get_campaign(seed)
rates = data_mod.get_rate_arrays(seed)
events: List[FailureEvent] = []
# ---- classification flips (candidate wrong, baseline right) ------
flips: Dict[Tuple[str, str], List] = defaultdict(list)
for obs in campaign.observations:
if (obs.candidate.predicted_class != obs.gt_class
and obs.baseline.predicted_class == obs.gt_class):
flips[(obs.gt_class, obs.candidate.predicted_class)].append(obs)
for (gt_cls, pred_cls), members in sorted(flips.items()):
safety = gt_cls in SAFETY_CRITICAL
if not safety and len(members) < 3:
continue # noise floor for non-safety patterns
if gt_cls == "pedestrian" and pred_cls == "construction_cone":
basis = DetectionBasis(
method="paired candidate-vs-GT scan + rate-population "
"telemetry denominator",
candidate_events=int(rates.candidate_flip.sum()),
baseline_events=int(rates.baseline_flip.sum()),
denominator=rates.n,
candidate_rate=float(rates.candidate_flip.mean()),
baseline_rate=float(rates.baseline_flip.mean()),
notes=[f"{len(members)} concrete scene instances captured "
"in the campaign for snippet/replay evidence",
"rate telemetry provides the population denominator"])
else:
n_cls = sum(1 for o in campaign.observations if o.gt_class == gt_cls)
b_ev = sum(1 for o in campaign.observations
if o.gt_class == gt_cls
and o.baseline.predicted_class == pred_cls)
basis = DetectionBasis(
method="paired candidate-vs-GT scan (campaign only)",
candidate_events=len(members), baseline_events=b_ev,
denominator=n_cls,
candidate_rate=len(members) / max(n_cls, 1),
baseline_rate=b_ev / max(n_cls, 1))
instances = [FailureInstance(
instance_id=new_id("inst"),
sequence_id=o.sequence_id, frame_id=o.frame_id,
frame_index=o.frame_index,
object_instance_id=o.object_instance_id,
gt_class=o.gt_class,
predicted_class=o.candidate.predicted_class,
confidence=o.candidate.confidence,
distance_m=o.distance_m,
construction_zone=o.construction_zone,
time_of_day=o.time_of_day, weather=o.weather,
geo_bucket=o.geo_bucket, occluded=o.occluded,
has_planner_trace=o.has_planner_trace,
) for o in members]
events.append(FailureEvent(
failure_id=new_id("fail"),
kind="classification_flip",
title=f"{gt_cls} misclassified as {pred_cls}",
description=(f"Candidate {campaign.candidate_model} predicts "
f"{pred_cls} where GT and baseline say {gt_cls}."),
gt_class=gt_cls, predicted_class=pred_cls,
detection_basis=basis,
instances=instances,
baseline_model=campaign.baseline_model,
candidate_model=campaign.candidate_model,
dataset_fingerprint=data_mod.campaign_fingerprint(campaign),
population_id=f"rate-pop-{seed}-n{rates.n}",
))
# ---- metric regressions ------------------------------------------
summary = self._metric_scan(campaign)
for kind, title, delta, threshold, extra in summary["checks"]:
if abs(delta) <= threshold:
continue
events.append(FailureEvent(
failure_id=new_id("fail"),
kind=kind, title=title,
description=f"metric delta {delta:+.4f} exceeded threshold "
f"{threshold} on the paired campaign scan",
detection_basis=DetectionBasis(
method="paired campaign metric scan",
candidate_events=0, baseline_events=0,
denominator=len(campaign.observations),
candidate_rate=0.0, baseline_rate=0.0,
metric_deltas={title: round(delta, 5), **extra}),
baseline_model=campaign.baseline_model,
candidate_model=campaign.candidate_model,
dataset_fingerprint=data_mod.campaign_fingerprint(campaign),
population_id=f"campaign-{seed}",
))
return events, summary
def _metric_scan(self, campaign: data_mod.Campaign) -> Dict:
obs = campaign.observations
def center_err(pred, gt):
return float(np.hypot(pred[0] - gt[0], pred[1] - gt[1]))
cand_loc = float(np.mean([center_err(o.candidate.bbox_3d, o.gt_bbox_3d)
for o in obs]))
base_loc = float(np.mean([center_err(o.baseline.bbox_3d, o.gt_bbox_3d)
for o in obs]))
cand_conf = float(np.mean([o.candidate.confidence for o in obs
if o.candidate.predicted_class == o.gt_class]))
base_conf = float(np.mean([o.baseline.confidence for o in obs
if o.baseline.predicted_class == o.gt_class]))
cand_acc = float(np.mean([o.candidate.predicted_class == o.gt_class
for o in obs]))
base_acc = float(np.mean([o.baseline.predicted_class == o.gt_class
for o in obs]))
def churn(model: str) -> float:
by_track: Dict[str, List[str]] = defaultdict(list)
for o in sorted(obs, key=lambda x: x.frame_index):
pred = o.candidate if model == "candidate" else o.baseline
by_track[f"{o.sequence_id}:{o.object_instance_id}"].append(
pred.predicted_class)
switches = total = 0
for classes in by_track.values():
for a, b in zip(classes, classes[1:]):
total += 1
switches += a != b
return switches / max(total, 1)
cand_churn, base_churn = churn("candidate"), churn("baseline")
return {
"observations": len(obs),
"candidate_accuracy": round(cand_acc, 5),
"baseline_accuracy": round(base_acc, 5),
"checks": [
("localization_regression", "mean_center_error_delta_m",
cand_loc - base_loc, LOCALIZATION_DELTA_THRESHOLD_M,
{"candidate_m": round(cand_loc, 4), "baseline_m": round(base_loc, 4)}),
("confidence_calibration_regression",
"mean_correct_confidence_delta",
cand_conf - base_conf, CONFIDENCE_DELTA_THRESHOLD,
{"candidate": round(cand_conf, 4), "baseline": round(base_conf, 4)}),
("detection_regression", "classification_accuracy_delta",
base_acc - cand_acc, 0.01,
{"candidate": round(cand_acc, 4), "baseline": round(base_acc, 4)}),
("temporal_regression", "track_class_churn_delta",
cand_churn - base_churn, CHURN_DELTA_THRESHOLD,
{"candidate": round(cand_churn, 4), "baseline": round(base_churn, 4)}),
],
}
# BaseAgent plumbing (detect() is the natural API; _analyze wraps it).
def _analyze(self, failure_id: str, **inputs):
events, summary = self.detect(inputs.get("seed", data_mod.DEFAULT_SEED))
output = {
"events_detected": len(events),
"titles": [e.title for e in events],
"scan_summary": {k: v for k, v in summary.items() if k != "checks"},
}
conf = 0.9 if events else 0.6
return (output, conf,
"deterministic paired scan; confidence reflects scan coverage",
no_escalation())
def _llm_prompt(self, failure_id, output, **inputs):
return ("Summarize this perception failure detection scan for an "
"engineering audience: " + compact(output))
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