# EPSM: A Novel Metric to Evaluate the Safety of Environmental Perception in Autonomous Driving

arXiv: https://arxiv.org/abs/2512.15195 · HTML: https://mlsanity.com/paper/2512.15195/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 17 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017)
- [End-to-End Multi-View Fusion for 3D Object Detection in LiDAR Point Clouds](https://mlsanity.com/paper/1910.06528/) (2019)
- [DeepAccident: A Motion and Accident Prediction Benchmark for V2X Autonomous Driving](https://mlsanity.com/paper/2304.01168/) (2023)
- [Point Density-Aware Voxels for LiDAR 3D Object Detection](https://mlsanity.com/paper/2203.05662/) (2022)
- [Towards Driving-Oriented Metric for Lane Detection Models](https://mlsanity.com/paper/2203.16851/) (2022)
- [LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving](https://mlsanity.com/paper/2407.07740/) (2024)
- [A Comprehensive Safety Metric to Evaluate Perception in Autonomous Systems](https://mlsanity.com/paper/2512.14367/) (2025)
- [CoLD Fusion: A Real-time Capable Spline-based Fusion Algorithm for Collective Lane Detection](https://mlsanity.com/paper/2512.14355/) (2025)

## Built on by (most cited first, 1 of 1)

- [Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving](https://mlsanity.com/paper/2609.11947/) (2026)

## Abstract (arXiv)

Extensive evaluation of perception systems is crucial for ensuring the safety of intelligent vehicles in complex driving scenarios. Conventional performance metrics such as precision, recall and the F1-score assess the overall detection accuracy, but they do not consider the safety-relevant aspects of perception. Consequently, perception systems that achieve high scores in these metrics may still cause misdetections that could lead to severe accidents. Therefore, it is important to evaluate not only the overall performance of perception systems, but also their safety. We therefore introduce a novel safety metric for jointly evaluating the most critical perception tasks, object and lane detection. Our proposed framework integrates a new, lightweight object safety metric that quantifies the potential risk associated with object detection errors, as well as an lane safety metric including the interdependence between both tasks that can occur in safety evaluation. The resulting combined safety score provides a unified, interpretable measure of perception safety performance. Using the DeepAccident dataset, we demonstrate that our approach identifies safety critical perception errors that conventional performance metrics fail to capture. Our findings emphasize the importance of safety-centric evaluation methods for perception systems in autonomous driving.