A road hypnosis identification method for drivers based on fusion of biological characteristics

Abstract

Risky driving behaviors, such as driving fatigue and distraction have recently received more attention. There is also much research about driving styles, driving emotions, older drivers, drugged driving, DUI (driving under the influence), and DWI (driving while intoxicated). Road hypnosis is a special behavior significantly impacting traffic safety. However, there is little research on this phenomenon. Road hypnosis, as an unconscious state, is can frequently occur while driving, particularly in highly predictable, monotonous, and familiar environments. In this paper, vehicle and virtual driving experiments are designed to collect the biological characteristics including eye movement and bioelectric parameters. Typical scenes in tunnels and highways are used as experimental scenes. LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbor) are employed as the base learners, while SVM (Support Vector Machine) serves as the meta-learner. A road hypnosis identification model is proposed based on ensemble learning, which integrates bioelectric and eye movement characteristics. The proposed model has good identification performance, as seen from the experimental results. In this study, alternative methods and technical support are provided for real-time and accurate identification of road hypnosis.

Graphical abstract

Keywords

Road hypnosis / State identification / Active safety / Drivers / Intelligent vehicles