Towards real-time monitoring of fear in driving sessions

Recent statistical analysis on road transport report that human behaviour represents one of the main causes in road traffic accidents. In this context, the application of new technologies to monitor driver's conditions becomes essential to detect anomalous driver behaviour and to identify near miss accidents. Near miss accidents are unplanned events that did not result in injury, illness, or damage but had the potential to do so. In dangerous goods transport by road, few accidents are reported but their consequences may be very relevant. So, the automatic detection of near miss accident may constitute an important resource to improve risk analysis. Specifically, the fundamental hypothesis of our research is that in a near miss accident, the driver's physiological conditions vary. Among them, due to either the emotion or the fear due to a miss accident, the driver's brain shows a variation which could be detected in real time.In this paper, an electroencephalogram (EEG)-based driver control system (EEG-DCS) is presented to monitor the driver brain activities. This study investigates the driver's behaviour and reaction when he/she is exposed to unexpected acoustic or visual external event which perturbs a car driving session in a virtual simulated scenario. The EEG Enobio cap with eight electrodes is used to perform EEG driver's monitoring. Signals related to alpha waves in the EEG signals have been evaluated in time and frequency domain The analysis has clearly demonstrated that the changes in driver's brain wave activities, visualized in the EEG, are significantly correlated to external events causing an unexpected fear to the driver. Copyright (C) 2019. The Authors. Published by Elsevier Ltd. All rights reserved.