Heterogeneous overtaking and learning styles with varied EEG patterns in a reinforced driving task

Abstract

Overtaking maneuvers occur when vehicle drivers pursue higher driving speeds or comfort scenarios through back-to-back lane-changing behaviors, which require active participation of mental resources and certain self-learning practices. However, few studies have investigated how brain activities change during overtaking. Moreover, the learning process, which indicates the heterogeneity of drivers from a process-based perspective, has been neglected. In this work, we studied varied overtaking and learning styles using electroencephalogram (EEG) signals collected from drivers during a simulated driving task with a possible learning process. The average speed, standard deviation of speed, steering wheel angle and lateral movement distance of overtaking behaviors are analyzed in these reinforced tasks to evaluate overtaking performance. Four types of overtaking styles (i.e., low-speed type, low-speed & strong-oscillation type, high-speed & strong-steering type, and high-speed & close-distance type) and three types of learning styles (i.e., stable, adaptive and changeful) are discovered, not only from eventual overtaking behaviors but also from behavioral changes in a certain learning process. EEG features, such as the power spectral density (PSD) in the θ, α, β and γ bands, are extracted to characterize driver mental states and to correlate with heterogeneous learning styles. The obtained results show that fatigue and fatigue confrontation are more likely with a stable learning style, and the mental workload is reduced with an adaptive learning style, whereas no significant changes in brain activity are apparent with a changeful learning style. Understanding and recognizing heterogeneous overtaking and learning styles with varying EEG patterns will be extremely useful in the future for deep integration of advanced driving assistance systems (ADASs) and brain computer interface (BCI) systems.