Nikmanesh Mohammadamin, Cinelli Michael E, Marigold Daniel S
Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.
Institute for Neuroscience and Neurotechnology, Simon Fraser University, Burnaby, BC, V5A 1S6, Canada.
Sci Rep. 2025 Jan 28;15(1):3530. doi: 10.1038/s41598-025-88149-3.
Busy walking paths, like in a park, city centre, or shopping mall, frequently necessitate collision avoidance behaviour. Lab-based research has shown how different situation- and person-specific factors, typically studied independently, affect avoidance behaviour. What happens in the real world is unclear. Thus, we filmed unscripted pedestrian walking behaviours on a busy urban path. We leveraged deep learning algorithms to identify and extract pedestrian walking trajectories and had unbiased raters characterize situations where two pedestrians approached each other from opposite ends. We found that smaller medial-lateral distance between approaching pedestrians and smaller crowd size predicted an increased likelihood of a subsequent path deviation. Furthermore, we found that whether a pedestrian looked distracted or held, pushed, or pulled an object predicted medial-lateral distance between pedestrians at time of crossing. Our results highlight both similarities and differences with lab-based behaviour and offer insights relevant to developing accurate computational models for realistic pedestrian movement.
繁忙的步行路径，如公园、市中心或购物中心的路径，经常需要避免碰撞行为。基于实验室的研究已经表明，不同的情境和个体特定因素（通常是独立研究的）如何影响回避行为。在现实世界中会发生什么尚不清楚。因此，我们在一条繁忙的城市道路上拍摄了未经编排的行人行走行为。我们利用深度学习算法来识别和提取行人的行走轨迹，并让无偏见的评估者对两名行人从相反两端相向而行的情况进行描述。我们发现，接近的行人之间较小的横向距离和较小的人群规模预示着随后路径偏离的可能性增加。此外，我们发现行人是否看起来注意力分散或手持、推或拉物体，预示着交叉时行人之间的横向距离。我们的结果突出了与基于实验室行为的异同，并为开发逼真的行人运动精确计算模型提供了相关见解。
