Towards Safety-Aware Pedestrian Detection in Autonomous Systems

In this paper, we present a framework to assess the quality of a pedestrian detector in an autonomous driving scenario. To do this, we exploit performance metrics from the domain of computer vision on one side and so-called threat metrics from the motion planning domain on the other side. Based on a reachability analysis that accounts for the uncertainty in future motions of other traffic participants, we can determine the worst-case threat from the planning domain and relate it to the corresponding detection from the visual input. Our evaluation results for a RetinaNet on the Argoverse 1.1 [1] dataset show that already a rather simple threat metric such as time-to-collision (TTC) allows to select potentially dangerous interactions between the ego vehicle and a pedestrian when purely vision-based detections fail, even if they are passed to a subsequent object tracker. In addition, our results show that two different DNNs (Deep Neural Networks) with comparable performance differ significantly in the number of critical scenarios that we can identify with our method.