Driving skill analysis using machine learning: The full curve and curve segmented cases

Naiwala P. Chandrasiri, Kazunari Nawa, Akira Ishii, Shuguang Li, Shigeyuki Yamabe, Takayuki Hirasawa, Yoichi Sato, Yoshihiro Suda, Takeshi Matsumura, Koji Taguchi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2012 12th International Conference on ITS Telecommunications, ITST 2012
Pages542-547
Number of pages6
DOIs
Publication statusPublished - 2012 Dec 1
Externally publishedYes
Event2012 12th International Conference on ITS Telecommunications, ITST 2012 - Taipei, Taiwan, Province of China
Duration: 2012 Nov 52012 Nov 8

Publication series

Name2012 12th International Conference on ITS Telecommunications, ITST 2012

Conference

Conference2012 12th International Conference on ITS Telecommunications, ITST 2012
CountryTaiwan, Province of China
CityTaipei
Period12/11/512/11/8

Keywords

  • Driving behavior
  • Driving simulator
  • Driving skill

ASJC Scopus subject areas

  • Computer Networks and Communications

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