Abstract
The advancement of automated driving technology has led to new challenges in the interaction between automated vehicles and human road users. However, there is currently no complete theory that explains how human road users interact with vehicles, and studying them in real-world settings is often unsafe and time-consuming. This study proposes a 3D Virtual Reality (VR) framework for studying how pedestrians interact with human-driven vehicles. The framework uses VR technology to collect data in a safe and cost-effective way, and deep learning methods are used to predict pedestrian trajectories. Specifically, graph neural networks have been used to model pedestrian future trajectories and the probability of crossing the road. The results of this study show that the proposed framework can be for collecting high-quality data on pedestrian-vehicle interactions in a safe and efficient manner. The data can then be used to develop new theories of human-vehicle interaction and aid the Autonomous Vehicles research.
Original language | English |
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Title of host publication | HRI 2024 - Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction |
Place of Publication | New York, USA |
Publisher | ACM |
Pages | 167-174 |
Number of pages | 8 |
ISBN (Electronic) | 9798400703225 |
DOIs | |
Publication status | Published - 11 Mar 2024 |
Event | 19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024 - Boulder, United States Duration: 11 Mar 2024 → 15 Mar 2024 |
Conference
Conference | 19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024 |
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Country/Territory | United States |
City | Boulder |
Period | 11/03/24 → 15/03/24 |
Keywords
- Autonomous Driving
- Human-Robot Interaction
- Unreal Engine
- Virtual Reality