# LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving

arXiv: https://arxiv.org/abs/2407.07740 · HTML: https://mlsanity.com/paper/2407.07740/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 10 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017)
- [Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection](https://mlsanity.com/paper/2010.12035/) (2020)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Ultra Fast Structure-aware Deep Lane Detection](https://mlsanity.com/paper/2004.11757/) (2020)
- [Real time Detection of Lane Markers in Urban Streets](https://mlsanity.com/paper/1411.7113/) (2014)
- [Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art](https://mlsanity.com/paper/2108.02403/) (2021)
- [Towards Driving-Oriented Metric for Lane Detection Models](https://mlsanity.com/paper/2203.16851/) (2022)
- [Dynamic Risk Assessment Methodology with an LDM-based System for Parking Scenarios](https://mlsanity.com/paper/2404.04040/) (2024)

## Built on by (most cited first, 4 of 4)

- [Criticality Metrics for Relevance Classification in Safety Evaluation of Object Detection in Automated Driving](https://mlsanity.com/paper/2512.15181/) (2025)
- [EPSM: A Novel Metric to Evaluate the Safety of Environmental Perception in Autonomous Driving](https://mlsanity.com/paper/2512.15195/) (2025)
- [SnowyLane: Robust Lane Detection on Snow-covered Rural Roads Using Infrastructural Elements](https://mlsanity.com/paper/2511.05108/) (2025)
- [Scenario-Independent Criticality Assessment and Prediction for Vulnerable Road Users in Autonomous Driving](https://mlsanity.com/paper/2609.11947/) (2026)

## Abstract (arXiv)

Comprehensive perception of the vehicle's environment and correct interpretation of the environment are crucial for the safe operation of autonomous vehicles. The perception of surrounding objects is the main component for further tasks such as trajectory planning. However, safe trajectory planning requires not only object detection, but also the detection of drivable areas and lane corridors. While first approaches consider an advanced safety evaluation of object detection, the evaluation of lane detection still lacks sufficient safety metrics. Similar to the safety metrics for object detection, additional factors such as the semantics of the scene with road type and road width, the detection range as well as the potential causes of missing detections, incorporated by vehicle speed, should be considered for the evaluation of lane detection. Therefore, we propose the Lane Safety Metric (LSM), which takes these factors into account and allows to evaluate the safety of lane detection systems by determining an easily interpretable safety score. We evaluate our offline safety metric on various virtual scenarios using different lane detection approaches and compare it with state-of-the-art performance metrics.