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

arXiv: https://arxiv.org/abs/2603.28029 · HTML: https://mlsanity.com/paper/2603.28029/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in End-to-end system architecture, one of 9 directions in End-to-end autonomous driving.
- Builds on 15 of its 28 references that are in the map.
- First posted on arXiv 30 Mar 2026.

## Placed in

- End-to-end autonomous driving / [End-to-end system architecture](https://mlsanity.com/driving/end-to-end-system-architecture/)

## Builds on (most cited first, 15 of 15)

- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation](https://mlsanity.com/paper/2205.13542/) (2022)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Learning to Evaluate Perception Models Using Planner-Centric Metrics](https://mlsanity.com/paper/2004.08745/) (2020)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)
- [Progressive Multi-Modal Fusion for Robust 3D Object Detection](https://mlsanity.com/paper/2410.07475/) (2024)
- [Safety-Aware Hardening of 3D Object Detection Neural Network Systems](https://mlsanity.com/paper/2003.11242/) (2020)
- [Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures](https://mlsanity.com/paper/2203.02205/) (2022)
- [Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations](https://mlsanity.com/paper/2305.19904/) (2023)
- [USC: Uncompromising Spatial Constraints for Safety-Oriented 3D Object Detectors in Autonomous Driving](https://mlsanity.com/paper/2209.10368/) (2022)
- [A Comprehensive Safety Metric to Evaluate Perception in Autonomous Systems](https://mlsanity.com/paper/2512.14367/) (2025)
- [PLM-Net: Perception Latency Mitigation Network for Vision-Based Lateral Control of Autonomous Vehicles](https://mlsanity.com/paper/2407.16740/) (2024)
- [A Flow-based Credibility Metric for Safety-critical Pedestrian Detection](https://mlsanity.com/paper/2402.07642/) (2024)
- [nuScenes Revisited: Progress and Challenges in Autonomous Driving](https://mlsanity.com/paper/2512.02448/) (2025)
- [Contour Errors: Ego-Centric Matching for 3D Multi-Object Tracking Performance Evaluation](https://mlsanity.com/paper/2506.04122/) (2025)

## Abstract (arXiv)

Criticality metrics such as time-to-collision (TTC) quantify collision urgency but do not distinguish the operational consequences of false-positive (FP) and false-negative (FN) perception errors. We formulate two error-specific effort metrics: False Speed Reduction (FSR), the cumulative velocity loss associated with persistent phantom detections, and Maximum Deceleration Rate (MDR), the peak braking demand associated with missed objects under a longitudinal kinematic model. These longitudinal metrics are complemented by Lateral Evasion Acceleration (LEA), adapted from prior lateral-evasion kinematics and coupled with reachability-based collision timing. The collision check quantifies the minimum steering effort required to avoid a predicted collision. A dynamically conservative, semantically unfiltered reachability gate selects candidate interactions before frame-level scoring and track-level aggregation. Evaluation on nuScenes and Argoverse 2 shows that 65% to 93% of errors fall below the chosen criticality thresholds. Correlation and threshold analysis indicate that the proposed metrics provide complementary rankings for screening and mining perception failures and are not substitutes for closed-loop safety validation.