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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.16315 |
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| _version_ | 1866909554574360576 |
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| author | Tan, Ashton Yu Xuan Yang, Yingkai Zhang, Xiaofei Li, Bowen Gao, Xiaorong Zheng, Sifa Wang, Jianqiang Gu, Xinyu Li, Jun Zhao, Yang Zhang, Yuxin Stathaki, Tania |
| author_facet | Tan, Ashton Yu Xuan Yang, Yingkai Zhang, Xiaofei Li, Bowen Gao, Xiaorong Zheng, Sifa Wang, Jianqiang Gu, Xinyu Li, Jun Zhao, Yang Zhang, Yuxin Stathaki, Tania |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Passenger hazard perception based on EEG signals for highly automated driving vehicles Tan, Ashton Yu Xuan Yang, Yingkai Zhang, Xiaofei Li, Bowen Gao, Xiaorong Zheng, Sifa Wang, Jianqiang Gu, Xinyu Li, Jun Zhao, Yang Zhang, Yuxin Stathaki, Tania Human-Computer Interaction Machine Learning Signal Processing 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. |
| title | Passenger hazard perception based on EEG signals for highly automated driving vehicles |
| topic | Human-Computer Interaction Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2408.16315 |