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Validity Evaluations of On-Line Driver Modeling and State Assessment

[+] Author Affiliations
Liang-Kuang Chen, Meng-Hsuan Peng

National Taiwan University of Science and Technology, Taipei, Taiwan

Paper No. IMECE2007-41798, pp. 151-157; 7 pages
  • ASME 2007 International Mechanical Engineering Congress and Exposition
  • Volume 9: Mechanical Systems and Control, Parts A, B, and C
  • Seattle, Washington, USA, November 11–15, 2007
  • Conference Sponsors: ASME
  • ISBN: 0-7918-4303-3 | eISBN: 0-7918-3812-9
  • Copyright © 2007 by ASME


Although driver steering control models and on-line model identification have been studied extensively, the application of the driver models to driver state assessment is seldom investigated. Furthermore, the validity level, or confidence index, of the on-line modeling and assessment of driver behavior is not reported in the literature. In this paper, on-line system identification techniques are applied to the determination of driver model parameters and model validity estimation. The driver steering control model is estimated on-line using system identification techniques. The on-line driver state assessment is achieved using probabilistic neural network (PNN), and the validity of the assessment is derived from the likelihood function inside the PNN. Preliminary results show that the computed validity indices agree with expectation reasonably well. More driving simulator experiments will be conducted to validate the proposed indices.

Copyright © 2007 by ASME
Topics: Modeling



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