The obscured pedestrian-motor vehicle crash has become a serious danger to driving safety. The present study aims to investigate the contingent negative variation (CNV) during the anticipation of an obscured pedestrian-motor vehicle crash in simulated driving. We adopted two cueing tasks: (i) a traditional cognitive paradigm of cueing task that has been widely used to study anticipatory process, and (ii) a modified cueing task in simulated driving scenes, in which Electroencephalogram (EEG) signals of 32 participants were recorded to detect the CNV. Simulated car following and pedestrian crossing tasks were designed to measure anticipation-related driving behaviors. The results showed that both early and late CNVs were observed in two cueing tasks. The mean amplitude of the late CNV during a modified cueing task in simulated driving was significantly larger than that in a traditional cueing task, which was not the case for the early CNV potentials. In addition, both early and late CNVs elicited in simulated driving were significantly correlated with anticipatory driving behaviors (e.g., the minimum time to collision). These findings show that CNV potentials during the anticipation of an obscured pedestrian-motor vehicle crash might predict anticipation-related risky driving behaviors.
Many potential hazards are encountered during daily driving in mixed traffic situations, and the anticipatory activity of a driver to a hazard is one of the key factors in many crashes. In a previous study using eye-tracking data, it was reliably recognized whether the eyes of a driver had become fixated or pursued hazard cues. A limitation of using eye-tracking data is that it cannot be identified whether the anticipatory activity of a driver to hazards has been activated. This study aimed to propose a method to recognize whether the psychological anticipation of a driver had been activated by a hazard cue using electroencephalogram (EEG) signals as input. Thirty-six drivers participated in a simulated driving task designed according to a standard psychological anticipatory study paradigm. Power spectral density (PSD) features were extracted from raw EEG data, and feature dimensions were reduced by principal component analysis (PCA). The results showed that when a driver detected a hazard cue, the alpha band immediately decreased, and the beta band increased approximately 300 ms after the cue appeared. Based on performance evaluation of the support vector machine (SVM), k-nearest neighbor (KNN) method, and linear discriminant analysis (LDA), SVM could detect the anticipatory activity of the driver to a potential hazard in a timely manner with an accuracy of 81%. The findings demonstrated that the hazard anticipatory activity of a driver could be recognized with EEG data as input.
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