arXiv 2402.025332024-02-04cs.RO

识别与提取临界交通情境中的行人行为

Identifying and Extracting Pedestrian Behavior in Critical Traffic Situations

Graz University of Technology（格拉茨工业大学）；Vehicle Safety Institute（车辆安全研究所）；Institute of Computer Graphics and Vision（计算机图形与视觉研究所）

Martin Schachner, Bernd Schneider, Fabian Weissenbacher, Nadezda Kirillova, Horst Possegger, Horst Bischof, Corina Klug

备注：: 7 pages, 8 figures, ITSC 2023 accepted

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AI总结：

提出一种结合后侵占时间与新运动适应度量的方法，从110小时视频中提取临界行人-车辆交互，最终识别出7个关键场景，用于行人行为建模。

摘要：

更好地理解临界交通情境中交互式行人行为对于开发增强型行人安全系统至关重要。真实世界的交通观测在此方面起着决定性作用，因为它们以无偏见的方式呈现行为。在本工作中，我们提出了一种方法，可从基于摄像头的观测系统中提取出相当重要的行人-车辆交互子集。为此，我们利用110小时的视频记录，自动检查道路使用者轨迹以建立时空关系。为了识别临界交互，我们的方法将度量后侵占时间与新引入的运动适应度量相结合。从超过11,000条重建的行人轨迹中，使用2秒的后侵占时间阈值，保留了259个潜在场景。然而，在95%的情况下，未观察到行人因避免临界状态而做出的行为适应。应用所提出的运动适应度量后，仅剩21个临界场景。手动调查显示，其中7个场景存在临界行人-车辆交互。这些场景被进一步分析并公开提供，用于开发行人行为模型。结果表明，所提方法能够提取出行人在相对较晚阶段感知并对车辆做出反应的临界交互。

英文摘要：

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.

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