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Evaluation of Vehicle/Driver Performance Using Genetic Algorithms
ISSN: 0148-7191, e-ISSN: 2688-3627
Published February 23, 1998 by SAE International in United States
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Simulation is often used to gain an understanding of vehicle directional response. Furthermore, it is widely agreed that, given an adequate set of parameters that model the vehicle and the surface it drives on, it is reasonable to simulate a particular vehicle with a view toward understanding and perhaps improving its performance. This is not the case with the vehicle/driver system. Rather, in terms of a particular vehicle and driver, simulations provide interesting but not particularly reliable results because of the routine variability of the human part of the system.
Genetic algorithms and their derivatives are algorithms with their form drawn from the biological theory of evolution. This paper suggests that genetic algorithms may be useful to evaluate certain important facets of vehicle/driver performance. It supports this suggestion with an example that attempts to answer this question: What is the best a vehicle/driver system could do in the so-called Consumer Union short course? The example is challenging because the strategy the driver uses to drive through the course affects the result. The genetic-algorithm-based solution to this example problem provides evidence that the technique is promising.
The paper concludes with speculation on the potential for applying genetic algorithms in a much less constrained set of circumstances, including determination of the possibility of untripped rollover on a smooth surface.
CitationBernard, J., Gruening, J., and Hoffmeister, K., "Evaluation of Vehicle/Driver Performance Using Genetic Algorithms," SAE Technical Paper 980227, 1998, https://doi.org/10.4271/980227.
- “Not Acceptable: Isuzu Trooper/Acura SLX,” Consumer Reports October 1996
- Holland, J. 1975 Adaptation in Natural and Artificial Systems MIT Press Cambridge, Mass
- Goldberg, D.E. 1989 Genetic Algorithms in Search, Optimization, and Machine Learning Addison-Wesley Reading, Mass
- Walker, John F. “Evolution of Simple Virtual Robots Using Genetic Algorithms.” Master's Thesis Iowa State University Ames, Iowa 1995
- Ashlock, Daniel A. “Optimization and Modeling with Artificial Life.” Manuscript Iowa State University, Department of Mathematics 1997
- Hoffmeister, Kurt M. Bernard, James E. “Tread Pitch Arrangement Optimization Through the Use of a Genetic Algorithm,” Tire Science and Technology March 1997
- Bernard, J.E. Bhatnager, A. Clover, C.L. “Evaluation of Select Vehicle Dynamics Models - Phase II Final Report,” Motor Vehicle Manufactures Association 1992
- Bernard, J.E. Clover, C.L. “Tire Modeling for Low-Speed and High-Speed Calculations,” SAE Paper 950311 1995
- Allen, R.W Szostak, H.T. Rosenthal, T.J. et al. “Vehicle Dynamic Stability and Rollover,” Systems Technology, Inc.