Related Experiment Video
Updated: May 30, 2025
06:38
Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
Published on: June 9, 2020
4.8K
Identifying factors that contribute to collision avoidance behaviours while walking in a natural environment
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
Summary
Pedestrians adjust their walking paths to avoid collisions, especially in crowded areas. Distractions and carrying objects influence how closely people pass each other in real-world urban environments.
Area of Science:
- Human-computer interaction
- Robotics
- Urban planning
- Computer vision
Background:
- Collision avoidance behavior is crucial in crowded pedestrian environments like parks and city centers.
- Previous research on pedestrian behavior has primarily been lab-based, limiting real-world applicability.
- The impact of real-world environmental factors on pedestrian collision avoidance remains unclear.
Purpose of the Study:
- To investigate real-world pedestrian collision avoidance behaviors in an unscripted urban setting.
- To identify factors influencing pedestrian path deviation and proximity during encounters.
- To compare real-world findings with existing laboratory-based pedestrian behavior models.
Main Methods:
- Filmed unscripted pedestrian walking behavior on a busy urban path.
- Utilized deep learning algorithms to extract pedestrian trajectories.
- Employed unbiased raters to characterize pedestrian approach situations and behaviors.
Main Results:
- Smaller medial-lateral distances and reduced crowd sizes predicted increased path deviations.
- Pedestrian distraction and object manipulation (holding, pushing, pulling) affected proximity during crossing.
- Observed similarities and differences between real-world and lab-based pedestrian avoidance behaviors.
Conclusions:
- Real-world pedestrian collision avoidance is influenced by environmental density and individual factors.
- Findings provide valuable data for enhancing the accuracy of computational pedestrian movement models.
- Insights can inform the design of safer and more efficient urban public spaces.
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