Analysis of drivers’ characteristic driving operations based on combined features

Purpose Analysis of characteristic driving operations can help develop supports for drivers with different driving skills. However, the existing knowledge on analysis of driving skills only focuses on single driving operation and cannot reflect the differences on proficiency of coordination of driving operations. Thus, the purpose of this paper is to analyze driving skills from driving coordinating operations. There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature. A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method. Design/methodology/approach AdaBoost was used to extract features and the combined features method was used to combine two or more different driving operations at the same location. Findings A series of experiments based on driving simulator and specific course with several different curves were carried out, and the result indicated the feasibility of analyzing driving behavior through AdaBoost and the combined features method. Originality/value There are two main contributions: the first involves a method for feature extraction based on AdaBoost, which selects features critical for coordinating operations of experienced drivers and inexperienced drivers, and the second involves a generating method for candidate features, called the combined features method, through which two or more different driving operations at the same location are combined into a candidate combined feature.

[1]  Akira Ishii,et al.  Driving skill classification in curve driving scenes using machine learning , 2016, Journal of Modern Transportation.

[2]  Mohan M. Trivedi,et al.  Driver classification and driving style recognition using inertial sensors , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[3]  Yi Lu Murphey,et al.  Driver's style classification using jerk analysis , 2009, 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems.

[4]  Chris Bingham,et al.  Impact of driving characteristics on electric vehicle energy consumption and range , 2012 .

[5]  Hiok Chai Quek,et al.  Driving Profile Modeling and Recognition Based on Soft Computing Approach , 2009, IEEE Transactions on Neural Networks.

[6]  Junqiang Xi,et al.  Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches , 2017, IEEE Transactions on Intelligent Transportation Systems.

[7]  Jonas Sjöberg,et al.  Online driver behavior classification using probabilistic ARX models , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[8]  Yoshihiro Suda,et al.  Learning Characteristic Driving Operations in Curve Sections that Reflect Drivers’ Skill Levels , 2014, Int. J. Intell. Transp. Syst. Res..

[9]  Takeshi Mita,et al.  Joint Haar-like features for face detection , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[10]  Lin Li,et al.  Driving Style Classification Using a Semisupervised Support Vector Machine , 2017, IEEE Transactions on Human-Machine Systems.

[11]  Yoichi Sato,et al.  Driving skill analysis using machine learning The full curve and curve segmented cases , 2012, 2012 12th International Conference on ITS Telecommunications.

[12]  Jonathan P. How,et al.  Driver Behavior Classification at Intersections and Validation on Large Naturalistic Data Set , 2012, IEEE Transactions on Intelligent Transportation Systems.

[13]  Fridulv Sagberg,et al.  A Review of Research on Driving Styles and Road Safety , 2015, Hum. Factors.

[14]  Yoichi Sato,et al.  Driving Feature Extraction from High and Low Skilled Drivers in Curve Sections Based on Machine Learning , 2013 .