A better understanding of interactive pedestrian behavior in critical traffic situations is essential for the development of enhanced pedestrian safety systems. Real-world traffic observations play a decisive role in this, since they represent behavior in an unbiased way. In this work, we present an approach of how a subset of very considerable pedestrian-vehicle interactions can be derived from a camera-based observation system. For this purpose, we have examined road user trajectories automatically for establishing temporal and spatial relationships, using 110h hours of video recordings. In order to identify critical interactions, our approach combines the metric post-encroachment time with a newly introduced motion adaption metric. From more than 11,000 reconstructed pedestrian trajectories, 259 potential scenarios remained, using a post-encroachment time threshold of 2s. However, in 95% of cases, no adaptation of the pedestrian behavior was observed due to avoiding criticality. Applying the proposed motion adaption metric, only 21 critical scenarios remained. Manual investigations revealed that critical pedestrian vehicle interactions were present in 7 of those. They were further analyzed and made publicly available for developing pedestrian behavior models3. The results indicate that critical interactions in which the pedestrian perceives and reacts to the vehicle at a relatively late stage can be extracted using the proposed method.

翻译：对关键交通情境中交互式行人行为的深入理解，对于开发增强型行人安全系统至关重要。真实世界交通观测因其能无偏呈现行为特征而具有决定性作用。本研究提出一种方法，通过基于摄像头的观测系统提取极具代表性的行人-车辆交互子集。为此，我们利用110小时视频记录对道路使用者轨迹进行自动分析，以建立时空关联。为了识别关键交互，该方法将度量后侵占时间与新引入的运动适应度量相结合。通过设置2秒的后侵占时间阈值，从超过11,000条重建行人轨迹中筛选出259个潜在场景。然而在95%的案例中，由于行人已规避危险，未观察到其行为适应现象。应用所提出的运动适应度量后，仅保留21个关键场景。人工核查显示其中7个场景存在关键性行人-车辆交互。这些场景经进一步分析后已公开，用于开发行人行为模型。结果表明，该方法能有效提取行人感知车辆并做出反应阶段相对较晚的关键交互。