Passenger hazard perception based on EEG signals for highly automated driving vehicles

• URL: http://arxiv.org/abs/2408.16315v1
• Date: Thu, 29 Aug 2024 07:32:30 GMT
• Title: Passenger hazard perception based on EEG signals for highly automated driving vehicles
• Authors: 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,
• Abstract summary: 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. 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.
• Score: 23.322910031715583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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