Testing the Validity of Multiparticipant Distributed Simulation for Understanding and Modeling Road User Interaction
Artikel i vetenskaplig tidskrift, 2025

Understanding driver–pedestrian interactions at unsignalized locations has gained additional importance due to recent advancements in vehicle automation. Naturalistic observations can only provide correlational data of limited value for understanding and modeling the mechanisms underlying road user interaction. Therefore, controlled studies in virtual reality (VR) are an important complement, but conventional methods can only accommodate a single human participant. Recently, there has been some interest in studying interactions in VR, by means of distributed simulation, involving multiple human participants. However, there is a lack of validation of this method. Here, we provide a validation study, focusing on a distributed vehicle–pedestrian interaction setup, where pairs of one driver and one pedestrian interacted under various kinematic conditions in a connected virtual environment. To test the validity of the distributed simulation, we used a naturalistic dataset collected in the same U.K. city, at similar locations, and compared the observed behavior between the two settings. Our results indicate a good relative validity of the simulator study, where road users showed similar nonverbal communication behavior in both datasets. As an additional means of validation, we also leveraged a set of game theoretic models that were developed based on the simulator studies, and found that when applied to the naturalistic dataset, we obtained similar (although not identical) model selection results. The findings suggest that distributed simulation can also be useful for development of computational models of interaction. Overall, the findings suggest that distributed simulation can be a highly valuable tool for studying and modeling road user interactions.

vehicle driving

decision making

human–computer interaction

Behavioral sciences

mathematical models

Författare

Amir Hossein Kalantari

University of Leeds

TU Delft

Yi Shin Lin

University of Leeds

Ali Mohammadi

Chalmers, Mekanik och maritima vetenskaper, Fordonssäkerhet

Natasha Merat

University of Leeds

Gustav M Markkula

University of Leeds

IEEE Transactions on Human-Machine Systems

2168-2291 (ISSN) 21682305 (eISSN)

Vol. In Press

Ämneskategorier (SSIF 2025)

Transportteknik och logistik

Infrastrukturteknik

Artificiell intelligens

DOI

10.1109/THMS.2025.3591506

Mer information

Senast uppdaterat

2025-08-22