Understanding Pedestrian Cognition Workload in Traffic Environments Using Virtual Reality and Electroencephalography

Abstract

Understanding pedestrians’ cognitive processes in traffic environments is crucial for developing strategies to enhance safety and reduce accidents. This study assesses the efficacy of virtual reality (VR) in evaluating pedestrian behavior in simulated road-crossing scenarios. It investigates VR’s capability to realistically mimic the cognitive load experienced in real-world settings. It examines the technical integration of VR with psychophysiological recording to capture cognitive demand indicators accurately. Utilizing a dedicated VR application and electroencephalogram (EEG) measurements, this research aims to elicit significant Event-Related Potentials (ERP), like P3 and Contingent Negative Variation (CNV), associated with decision-making processes. The initial results demonstrate VR’s effectiveness in creating realistic environments for investigating cognitive mechanisms and the balance between induced immersion and experienced discomfort. Additionally, the tasks involving time-to-arrival estimations and oddball scenarios elicited the anticipated components related to attentional and decision-making processes. Despite increased discomfort with extended VR exposure, our results show that it did not negatively impact the cognitive workload. These outcomes highlight VR’s efficacy in replicating the cognitive demands of real-world settings and provide evidence to understand the neurophysiological and behavioral dynamics of vulnerable road users (VRUs) in traffic scenarios. Furthermore, these findings support VR’s role in behavioral and neurophysiological research to design specific safety interventions for VRUs.