Passenger hazard perception based on EEG signals for highly automated driving vehicles
This work addresses safety for passengers in autonomous vehicles by integrating neural mechanisms, though it appears incremental as it builds on existing EEG and neural network methods.
The study tackled the problem of improving safety in highly automated driving vehicles by developing a Passenger Cognitive Model and EEG decoding strategy, achieving an accuracy of 85.0% ± 3.18% in predicting hazardous scenarios from pre-event EEG data.
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.