[2408.16315] Passenger hazard perception based on EEG signals for highly automated driving vehicles
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Computer Science > Human-Computer Interaction

arXiv:2408.16315 (cs)
This paper has been withdrawn by Ashton Yu Xuan Tan Mr
[Submitted on 29 Aug 2024 (v1), last revised 27 Mar 2025 (this version, v2)]

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
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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.
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
Subjects: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Signal Processing (eess.SP)
Cite as: arXiv:2408.16315 [cs.HC]
  (or arXiv:2408.16315v2 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2408.16315

Submission history

From: Ashton Yu Xuan Tan Mr [view email]
[v1] Thu, 29 Aug 2024 07:32:30 UTC (36,521 KB)
[v2] Thu, 27 Mar 2025 13:34:40 UTC (1 KB) (withdrawn)
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