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.

翻译：对车辆环境的全面感知及对环境的正确解读对于自动驾驶车辆的安全运行至关重要。对周围物体的感知是执行轨迹规划等后续任务的主要组成部分。然而，安全的轨迹规划不仅需要物体检测，还需要对可行驶区域及车道走廊进行检测。虽然已有初步研究考虑了物体检测的高级安全评估，但车道线检测的评估仍缺乏充分的安全指标。与物体检测的安全指标类似，评估车道线检测时应考虑额外因素，例如包含道路类型和宽度的场景语义、检测范围，以及由车速等因素导致的漏检潜在原因。因此，我们提出了车道安全指标（LSM），该指标综合考虑了这些因素，并通过确定一个易于解释的安全评分来评估车道线检测系统的安全性。我们在多种虚拟场景中使用不同的车道线检测方法对所提出的离线安全指标进行了评估，并将其与最先进的性能指标进行了比较。