Recognizing driving styles based on topic models

Abstract

With the explosion of information in our current era, senders of information increasingly need to target their messages to recipients. However, messages within transportation systems, including traffic information and commercial advertisements, tend to be transmitted to all drivers indiscriminately. This is because the information providers (such as other vehicles, roads, facilities, buildings etc.), can hardly recognize the variations within drivers, who should be treated differently as information recipients. As a result of the rapid development of data collection technologies and machine learning techniques in recent years, extraction and recognition of drivers’ unique driving style from actual driving behaviour data become possible. In this paper, two kinds of topic models are investigated: mLDA and mHLDA, to discover distinguishable driving style information with hidden structure from the real-world driving behaviour data. The results show that the proposed models can successfully recognize the differences between driving styles. The study is of great value for providing deep insight into the underlying structure of driving styles and can effectively support the recognition of drivers with different driving styles.