Effort-Based Criticality Metrics for Evaluating 3D Perception Errors in Autonomous Driving

• URL: http://arxiv.org/abs/2603.28029v1
• Date: Mon, 30 Mar 2026 04:46:03 GMT
• Title: Effort-Based Criticality Metrics for Evaluating 3D Perception Errors in Autonomous Driving
• Abstract summary: Criticality metrics such as time-to-collision quantify collision urgency but conflate the consequences of perception errors.<n>We propose two novel effort-based metrics: False Speed Reduction and Maximum Deceleration Rate.<n>We show that all three metrics capture safety-relevant information inaccessible to established time-based, deceleration-based, or normalized criticality measures.
• Score: 14.590459688780205
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Criticality metrics such as time-to-collision (TTC) quantify collision urgency but conflate the consequences of false-positive (FP) and false-negative (FN) perception errors. We propose two novel effort-based metrics: False Speed Reduction (FSR), the cumulative velocity loss from persistent phantom detections, and Maximum Deceleration Rate (MDR), the peak braking demand from missed objects under a constant-acceleration model. These longitudinal metrics are complemented by Lateral Evasion Acceleration (LEA), adapted from prior lateral evasion kinematics and coupled with reachability-based collision timing to quantify the minimum steering effort to avoid a predicted collision. A reachability-based ellipsoidal collision filter ensures only dynamically plausible threats are scored, with frame-level matching and track-level aggregation. Evaluation of different perception pipelines on nuScenes and Argoverse~2 shows that 65-93% of errors are non-critical, and Spearman correlation analysis confirms that all three metrics capture safety-relevant information inaccessible to established time-based, deceleration-based, or normalized criticality measures, enabling targeted mining of the most critical perception failures.

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