Understanding Individualization Driving States via Latent Dirichlet Allocation Model

Abstract

Automatic driving technology has become one of the hottest research topics of the Intelligent Transportation System (ITS) and Artificial Intelligence (AI) in the recent years. The development of automatic driving technology can be promoted through understanding driving states of each driver (individualization driving). Although some methods for driving states understanding are proposed by previous studies, the latent driving states and structured driving behaviors has not yet been automatically discovered. The purpose of this study is to develop an unsupervised method for deeply understanding the individualization driving. First, an encode method is proposed to extracted driving behavior from vehicle motion data. Then, Latent Dirichlet Allocation (LDA) model is innovatively developed to understand latent driving states and quantified structure of the driving behavior patterns (topics) from individualization driving (documents) using driving behaviors (words). In order to validate the performance and effectiveness of the proposed method, twenty-two drivers (15 males and 7 females) were recruited to carry out road experiments in Wuhan, China for experiments data collection. In addition, two typical unsupervised methods, including k-means and the random method are established and their performances are compared in our experiments. Experimental results verify the superiority of proposed method compared with other methods.