Stress-Aware Personalized Road Navigation System

Abstract

Driving can be a stressful task, especially under congestion conditions. Several studies have shown a positive correlation between stress and aggressive behaviour behind the wheel, leading to accidents. One common way to minimize stress while driving is to avoid highly congested roads. However, not all drivers show the same response towards high traffic situations or other road conditions. For instance, some drivers may prefer congested routes to longer ones to minimize travel time. Increasingly, drivers are employing Advanced Traveller Information Systems while commuting to both familiar and unfamiliar destinations, not just to obtain information on how to reach a certain endpoint, but to acquire real-time data on the state of the roads and avoid undesired traffic conditions. In this thesis, we propose an Advanced Traveller Information System that personalizes the driver’s route using their road preferences and measures their physiological signals during the trip to assess mental stress. The system then links road attributes, such as number of lanes, speed limit, and traffic severity, with the driver’s stress levels. Then, it uses machine learning to predict their stress levels on similar roads. Hence, routes that contribute to high-levels of stress can therefore be avoided in future trips. The average accuracy of the proposed stress level prediction model is 76.11%.