Cross-Scenario Vigilance Detection Based on EEG Analysis for Safety Driving in Autonomous

Safety driver vigilance is a prerequisite for the safe operation of autonomous vehicles. In contrast to vehicle behavioral trajectory detection, which suffers from high latency and low accuracy, vigilance detection based on physiological signals is currently the most reliable and accurate method. While vigilance monitoring methods using electroencephalograms (EEG) have made considerable progress in experimental scenarios, they remain a challenging problem in scenario-constrained conditions, such as high-speed moving autonomous vehicles. This is due to the low signal-to-noise ratio in EEG signal acquisition and the difficulty of real-time processing. Moreover, cumbersome data acquisition processes and the challenges of labeling have hindered progress in this area. Given the successful use of EEG for monitoring in experimental settings, we believe that the transfer of knowledge learned from these scenarios to new contexts is reasonably feasible. Thus, this work aims to bridge the domain gap between experimental and real-world scenarios while balancing the number of channels and accuracy. Specifically, we propose a framework for EEG vigilance detection capable of Cross-scenario, Cross-subject, and Cross-device, called CCC. The proposed framework leverages the standard montage structure of EEG channels, reducing the number of channels by considering the common regions of EEG channels across different scenarios. The results show that our proposed model achieves an average accuracy of 86.20% on the SEED-VIG dataset with 12 subjects, which is higher than the 82.21% achieved by state-of-the-art deep learning approaches. Finally, we investigate the role of the attention mechanism and transfer learning, and further attempt to explain the advantages of our proposed approach from a visualization perspective.