Driving behavior analysis of multiple information fusion based on AdaBoost

Abstract

With the increase in the number of private cars as well as the nonprofessional drivers, the current traffic environment is in urgent need of driving assist equipment to timely reminder and to rectify the incorrect driving behavior. In order to meet this requirement, this paper proposes an innovative algorithm of driving behavior analysis based on AdaBoost with a variety of driving operation and traffic information. The proposed driving behavior analysis algorithm will mainly monitor driver's driving operation behavior, including steering wheel angle, brake force, and throttle position. To increase the accuracy of driving behavior analysis, the proposed algorithm also takes road conditions into account. The proposed will make use of AdaBoost to create a driving behavior classification model in various different road conditions, and then could determine whether the current driving behavior belongs to safe driving. Experimental results show the correctness of the proposed driving behavior analysis algorithm can achieve average 80% accuracy in various driving simulations. The proposed algorithm has the potential of applying to real-world driver assistance system.