识别导致在自然环境中行走时避免碰撞的行为因素
Mohammadamin Nikmanesh1,2, Michael E Cinelli3, Daniel S Marigold4,5
1Department of Biomedical Physiology and Kinesiology, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada.
Scientific reports
|January 28, 2025
概括
步行者调整步行路径以避免碰撞,特别是在拥挤的地区. 分散注意力和携带物体会影响人们在现实城市环境中彼此的距离.
科学领域:
- 人与计算机的互动.
- 机器人技术 机器人技术 机器人技术
- 城市规划是城市规划.
- 计算机视觉 计算机视觉 计算机视觉
背景情况:
- 避免碰撞的行为在拥挤的行人环境中至关重要,如公园和城市中心.
- 以前对行人行为的研究主要是基于实验室,限制了现实世界的适用性.
- 现实环境因素对避免行人碰撞的影响尚不清楚.
研究的目的:
- 为了调查现实世界的行人避免碰撞行为在一个无脚本的城市环境.
- 识别影响行人路径偏差和相遇时的近距离因素.
- 将现实世界的发现与现有的基于实验室的行人行为模型进行比较.
主要方法:
- 在繁忙的城市路径上拍摄了无脚本的行人行走行为.
- 使用深度学习算法来提取行人轨迹.
- 雇佣了公正的评分员来描述行人接近的情况和行为.
主要成果:
- 较小的中侧边距离和减少的人群规模预测了道路偏差的增加.
- 步行者分散注意力和对象操纵 (持有,推,拉) 影响过路时的近距离.
- 观察到现实世界和实验室的行人避开行为之间的相似之处和差异.
结论:
- 在现实世界中,避免行人碰撞受到环境密度和个人因素的影响.
- 结果为提高计算行人运动模型的准确性提供了有价值的数据.
- 洞察力可以为设计更安全,更有效的城市公共空间提供信息.
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