On-the-Go Automated Break Recommendation for Stress Avoidance during Highway Driving

Abstract

Continuous cab driving is considered one of the highly stressful jobs, although the drivers ignore that many a time. Taking a break from manual driving or transferring the control to another driver to release the stress would be an easy, intuitive solution, although the challenge is to detect the driving stress while the trip is going on. As driving stress depends on multiple diverse environmental and affective features, we, in this paper, develop a novel assistive system, SmartHalt , which continuously senses the driving environment (like road type, congestion, driver’s driving pattern, etc.) and then utilizes a spatial time series model of the driving environment with a deep learning framework to predict whether the driver will be stressed while on the trip. The model also considers the personality traits of the drivers along with the spatio-temporal features to differentiate the impact of stress on the driving behaviour for different drivers and recommends taking a break soon before the driving behaviour drops below a critical level. A thorough analysis of the model over 7 different drivers for a 10 month-long experiment over 204871 km of driving data reveals that the proposed approach can significantly improve driving behaviour by recommending a driving break at proper times. Following the recommendation by SmartHalt improves the driving score by \(\approx 50\% \) and reduces the number of driving offences by \(\approx 50\% \) . SmartHalt can help develop advanced driving assisting system (ADAS) platforms that understand the affective states of the driver and thus can be helpful for semi-autonomous driving environments for effective driver-vehicle interactions.