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

• URL: http://arxiv.org/abs/2407.07740v1
• Date: Wed, 10 Jul 2024 15:11:37 GMT
• Title: LSM: A Comprehensive Metric for Assessing the Safety of Lane Detection Systems in Autonomous Driving
• Authors: Jörg Gamerdinger, Sven Teufel, Stephan Amann, Georg Volk, Oliver Bringmann,
• Abstract summary: We propose the Lane Safety Metric (LSM) to evaluate the safety of lane detection systems. Additional factors such as the semantics of the scene with road type and road width should be considered for the evaluation of lane detection. 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.
• Score: 0.5326090003728084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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