Real-time abnormal driving behaviors monitoring is a corner stone to improving driving safety. Existing works on driving behaviors monitoring using smartphones only provide a coarsegrained result, i.e. distinguishing abnormal driving behaviors from normal ones. To improve drivers’ awareness of their driving habits so as to prevent potential car accidents, we need to consider a finegrained monit...

This paper presents a stochastic driver behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using a Dirichlet process mixture model, as a non-parametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of ea...

Despite the potential benefits of autonomous vehicles (AVs) reducing human driver errors and enhancing traffic safety, a comprehensive evaluation recent AV collision data reveals concerning trend rear-end collisions caused by following vehicles. This study aimed to address this issue developing methodology that identifies relationship between driving patterns risk leading using spectral analysi...

PROBLEM To determine patterns of risk among teenage drivers. METHOD Review and synthesis of the literature. RESULTS On most measures, crash rates during the teenage years are higher than at any other age, for both males and females. Risk among teenagers varies greatly by driving situation; it is particularly low in some situations (e.g., the learner period) and particularly high in others (...