Detection of Vigilance in L3 Autonomous Driving Based on EEG

In L3 autonomous driving, the driver needs to keep supervising the road conditions and take over the vehicle in time when the system fails. However, the driver may not be able to quickly recover his driving ability due to fatigue or distraction. Vigilance is the ability to maintain attention and alertness. It is necessary to detect the driver's vigilance for the safety of L3 autonomous driving. Research on driving vigilance is mostly aimed at manual driving scenarios, and methods in related work are not feasible in the L3 autonomous driving. Reaction time, which is closely related to the Electroencephalogram (EEG) rhythm can be used as a measure for vigilance. In order to achieve the purpose of vigilance detection, this paper uses the wearable EEG device to collect the driver's EEG signal and predict the reaction time. However, the signal-to-noise ratio(SNR) of the EEG signal collected by the wearable EEG analyzer is very low. It is a challenge to extract effective features from EEG signals with low SNR and predict reaction time with low error. To solve this problem, this paper firstly extracts the power spectrum characteristics and time-frequency characteristics from the EEG signal. Secondly, the extracted characteristics are sent into Support Vector Regression(SVR) and Random Forest(RF) to predict the reaction time. In the simulation experiment, the prediction accuracy of our algorithm is nearly 90%, and the optimal RMSE can reach 100.19ms.