Driving behavior recognition and scoring: A Bayesian approach to driving simulator data analytics

Safety is a key factor in operating commercial motor vehicles (CMVs). Advanced driving assistance systems are designed to assist CMV drivers enhance driving safety and reduce the risk of accidents. This study proposes a framework for driving style recognition and introduces statistical features that enable modeling the behavioral risk of drivers in real-time. These features include main aspects such as speed, throttle, and steering wheel as well as the those surrounding traffic conditions. A full-scale, high-fidelity driving simulator is used to generate the driving data. The driving records are clustered into different groups and a Bayesian classifier is trained the training portion of data to compute the probability of belonging to aggressive or safe classes. The framework is then extended, under normality assumption and using the distance from the mean, to one that provides driving score based on proximity to the boundary between safe and risky driving. Finally, the accuracy and effectiveness of the framework are evaluated by calculating the confusion matrices and AUCs. The experimental results demonstrate that the proposed framework for driving style recognition achieves accuracy rates of 88% to 91% in the test portion of the driving simulator data set.