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 and Yuzhu Sun
Proceedings of the 2024 ACM/IEEE International Conference on Human Robot Interaction (HRI), 2024

Core A Conference H5-Index: 55# Core A Conference

A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection
‡ According to Core Ranking 2023"
# According to Google Scholar 2023"

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 timeconsuming. 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.

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BibTeX

@inproceedings{crosato24virtual,
 author={Crosato, Luca and Wei, Chongfeng and Ho, Edmond S. L. and Shum, Hubert P. H. and Sun, Yuzhu},
 booktitle={Proceedings of the 2024 ACM/IEEE International Conference on Human Robot Interaction},
 series={HRI '24},
 title={A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection},
 year={2024},
 pages={167--174},
 numpages={8},
 doi={10.1145/3610977.3634923},
 isbn={9.80E+12},
 publisher={ACM/IEEE},
 location={Colorado, USA},
}

RIS

TY  - CONF
AU  - Crosato, Luca
AU  - Wei, Chongfeng
AU  - Ho, Edmond S. L.
AU  - Shum, Hubert P. H.
AU  - Sun, Yuzhu
T2  - Proceedings of the 2024 ACM/IEEE International Conference on Human Robot Interaction
TI  - A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection
PY  - 2024
SP  - 167
EP  - 174
DO  - 10.1145/3610977.3634923
SN  - 9.80E+12
PB  - ACM/IEEE
ER  - 

Plain Text

Luca Crosato, Chongfeng Wei, Edmond S. L. Ho, Hubert P. H. Shum and Yuzhu Sun, "A Virtual Reality Framework for Human-Driver Interaction Research: Safe and Cost-Effective Data Collection," in HRI '24: Proceedings of the 2024 ACM/IEEE International Conference on Human Robot Interaction, pp. 167-174, Colorado, USA, ACM/IEEE, 2024.

Supporting Grants

Similar Research

Luca Crosato, Kai Tian, Hubert P. H. Shum, Edmond S. L. Ho, Yafei Wang and Chongfeng Wei, "Social Interaction-Aware Dynamical Models and Decision-Making for Autonomous Vehicles", Advanced Intelligent Systems (AIS), 2024
Luca Crosato, Hubert P. H. Shum, Edmond S. L. Ho and Chongfeng Wei, "Interaction-Aware Decision-Making for Automated Vehicles using Social Value Orientation", IEEE Transactions on Intelligent Vehicles (TIV), 2023
Luca Crosato, Chongfeng Wei, Edmond S. L. Ho and Hubert P. H. Shum, "Human-Centric Autonomous Driving in an AV-Pedestrian Interactive Environment Using SVO", Proceedings of the 2021 IEEE International Conference on Human-Machine Systems (ICHMS), 2021
Yuan Hu, Hubert P. H. Shum and Edmond S. L. Ho, "Multi-Task Deep Learning with Optical Flow Features for Self-Driving Cars", IET Intelligent Transport Systems (ITS), 2020

 

 

Last updated on 4 June 2024
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