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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.

Recent advances in computing power, computer graphics, and virtual reality systems are leading to important new opportunities for the development and use of driving simulators. These advances in technology are pointing towards a future where human-in-the-loop simulation is increasingly valuable for training, human-factors research, and virtual prototyping. This paper presents a general literature review of driving simulation, and discusses important components of modern driving simulators. The paper concludes with speculation on the future of driving simulation.

Today's computers offer the potential for increasingly complex models for use in simulation of ground vehicle dynamics The tire models that support these simulations are themselves quite complex, requiring a lengthy set of input parameters to support simulation of tires across a wide range of operating conditions This paper presents a platform-independent tire model preprocessor running under an X-Motif graphical interface it provides convenient input and graphical output for a range of user-specified tire operating conditions, and it allows graphical comparison with experimental data Currently supported are models from Calspan, Gim and Nikravesh, Pacejka, and Systems Technology, Inc (STI) The paper presents examples of coefficient checking, cross-model comparisons, and comments on the consequences of operating these models outside the range of their input data