Abstract: In order to improve driving safety and reduce traffic accidents, this paper proposes an extreme driving behavior recognition method based on mobile crowdsensing. The collected data of sensors related to users’ smart phones are preprocessed, and then the context information of passengers is identified by means of the dynamic step number detection and the random forest methods. According to different extreme driving behaviors, smart phones of passengers in different positions are selected for data collection, and the impact of different phone places and multi-feature fusion is also considered in designing the extreme driving behavior detection method. Regarding the potential inconsistency of the results from passengers in different positions, a Bayesian voting model is proposed to solve the problem. Experimental results from real world datasets indicate that our method can effectively identify the extreme driving behavior of drivers.

Key words: extreme driving behavior, context recognition, mobile crowdsensing, group decision