A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection

Luca Crosato, Chongfeng Wei, Edmond S.L. Ho, Hubert P.H. Shum, Yuzhu Sun

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 languageEnglish
Title of host publicationHRI 2024 - Proceedings of the 2024 ACM/IEEE International Conference on Human-Robot Interaction
Place of PublicationNew York, USA
Number of pages8
ISBN (Electronic)9798400703225
Publication statusPublished - 11 Mar 2024
Event19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024 - Boulder, United States
Duration: 11 Mar 202415 Mar 2024


Conference19th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2024
Country/TerritoryUnited States

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