  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.
