Driving style estimation by fusing multiple driving behaviors: a case study of freeway in China

Abstract

Traffic accident is one of the most serious issues in traffic problems. In China, more than 50 thousand people die in each year from traffic accidents. To alleviate the incidence of traffic accidents, this paper proposes a driving style estimation method by fusing multiple driving behaviors for Chinese drivers. Firstly, we invite Chinese volunteers to operate a driving simulator. Massive driving data are collected by the simulator. Then, a driving dataset is set up by the collected data. Furthermore, we adopt the collected driving data to represent behaviors by using SVM. Last but not least, a novel classification method is proposed to estimate driving styles, which is called multiple decision tree. The method can fuse multiple behaviors and explore the relationship between driving styles and behaviors. As a result, 20 volunteers and a freeway in China is selected for case study. After test, the proposed method has a 95% accuracy for style estimation. However, about 25% volunteers have a Risk style and these volunteers should change their driving habits. It also reveals the high incidence of accidents in China. Hence, the proposed method can alert the driver with bad styles and is helpful to ease traffic accidents.

Research on Classification and Recognition of Driving Styles Based on Feature Engineering

“…However, the subjective evaluation method based on simple logics may lead to certain subjectivity, and the driving style cannot be objectively defined. With the development of data mining and modern communication technologies, more and more driving data can be collected and numerous machine learning algorithms are employed to classify driving styles with improvement of rationality and accuracy [12], [13]. In [14], [15], the driver's behavioral characteristics are studied by collecting information from on-board GPS sensors and applying three different approaches, i.e., the DP-means algorithm, hidden Markov model (HMM), and behavioral topic extraction.…”

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