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

TL;DR

This paper introduces the Lane Safety Metric (LSM), a comprehensive evaluation tool for assessing the safety of lane detection systems in autonomous vehicles, considering scene semantics, detection range, and vehicle speed.

Contribution

The paper proposes the LSM, a novel safety metric for lane detection that incorporates multiple environmental and operational factors, addressing gaps in existing evaluation methods.

Findings

LSM provides an interpretable safety score for lane detection.

Evaluation on virtual scenarios demonstrates LSM's effectiveness.

LSM correlates well with real-world safety considerations.

Abstract

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.