Passenger hazard perception based on EEG signals for highly automated driving vehicles
<|reference_start|>Passenger hazard perception based on EEG signals for highly automated driving vehicles: 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 $85.0\% \pm 3.18\%$. 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.<|reference_end|>
@article{tan2024passenger,
    title={Passenger hazard perception based on EEG signals for highly automated
  driving vehicles},
    author={Ashton Yu Xuan Tan, Yingkai Yang, Xiaofei Zhang, Bowen Li, Xiaorong
  Gao, Sifa Zheng, Jianqiang Wang, Xinyu Gu, Jun Li, Yang Zhao, Yuxin Zhang,
  Tania Stathaki},
    journal={arXiv preprint arXiv:2408.16315},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16315},
    primaryClass={cs.HC cs.LG eess.SP}
}