Title :
Driving skill analysis using machine learning The full curve and curve segmented cases
Author :
Chandrasiri, Naiwala P. ; Nawa, Kazunari ; Ishii, A. ; Shuguang Li ; Yamabe, Shigeyuki ; Hirasawa, T. ; Sato, Yuuki ; Suda, Yoshiyuki ; Matsumura, Takeshi ; Taguchi, Katsuhisa
Author_Institution :
Toyota InfoTechnology Center, Co., Ltd., Toyota, Japan
Abstract :
Analysis of driving skill/driver state can be used in building driver support and infotainment systems that can be adapted to individual needs of a driver. In this paper we present a machine learning approach to analyzing driving maneuver skills of a driver that covers both longitudinal and lateral controls. The concept is to learn a driver model from sensor data that are related to driving environment, driving behavior and vehicle response. Once the model is built, driving skills of an unknown run can be classified automatically. In this paper, we demonstrate the feasibility of the proposed method for driving skill analysis based on a driving simulator experiment in a curve driving scene for both the full curve and curve segmented cases.
Keywords :
learning (artificial intelligence); traffic engineering computing; building driver support; curve driving scene; curve segmented cases; driving skill-driver state analysis; infotainment systems; machine learning approach; sensor data; Acceleration; Accuracy; Data analysis; Feature extraction; Principal component analysis; Support vector machines; Vehicles; Driving behavior; Driving simulator; Driving skill;
Conference_Titel :
ITS Telecommunications (ITST), 2012 12th International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4673-3071-8
Electronic_ISBN :
978-1-4673-3069-5
DOI :
10.1109/ITST.2012.6425238