Drivers Performance Evaluation using Physiological Measurement in a Driving Simulator

Monitoring the drivers behaviour and detecting their awareness are of vital importance for road safety. Drivers distraction and low awareness are already known to be the main reason for accidents in the world. Distraction-related crashes have greatly increased in recent years due to the proliferation of communication, entertainment, and malfunctioning of driver assistance systems. Accordingly, there is a need for advanced systems to monitor the drivers behaviour and generate a warning if a degradation in a drivers performance is detected. The purpose of this study is to analyse the vehicle and drivers data to detect the onset of distraction. Physiological measurements, such as palm electrodermal activity, heart rate, breathing rate, and perinasal perspiration are analysed and applied for the development of the monitoring system. The dataset used in this research has these measurements for 68 healthy participants (35 male, 33 female/17 elderly, 51 young). These participants completed two driving sessions in a driving simulator, including the normal and loaded drive. In the loaded scenario, drivers were texting back words. The lane deviation of vehicle was recorded as the response variable. Different classification algorithms such as generalised linear, support vector model, K-nearest neighbour and random forest machines are implemented to classify the driver's performance based on input features. Prediction results indicate that random forest performs the best by achieving an area under the curve (AUC) of over 91%. It is also found that biographic features are not informative enough to analyse drivers performance while perinasal perspiration carries the most information.