Enhancing the safety of autonomous vehicles is crucial, especially given recent accidents involving automated systems. As passengers in these vehicles, humans' sensory perception and decision-making can be integrated with autonomous systems to improve safety. This study explores neural mechanisms in passenger-vehicle interactions, leading to the development of a Passenger Cognitive Model (PCM) and the Passenger EEG Decoding Strategy (PEDS). Central to PEDS is a novel Convolutional Recurrent Neural Network (CRNN) that captures spatial and temporal EEG data patterns. The CRNN, combined with stacking algorithms, achieves an accuracy of 85.0%±3.18%. Our findings highlight the predictive power of pre-event EEG data, enhancing the detection of hazardous scenarios and offering a network-driven framework for safer autonomous vehicles.
@article{arxiv.2408.16315,
title = {Passenger hazard perception based on EEG signals for highly automated driving vehicles},
author = {Ashton Yu Xuan Tan and Yingkai Yang and Xiaofei Zhang and Bowen Li and Xiaorong Gao and Sifa Zheng and Jianqiang Wang and Xinyu Gu and Jun Li and Yang Zhao and Yuxin Zhang and Tania Stathaki},
journal= {arXiv preprint arXiv:2408.16315},
year = {2025}
}
Comments
We have decided to withdraw this submission due to ongoing revisions and further refinements in our research. A revised version may be resubmitted in the future. We appreciate the feedback and interest from the community