Evaluation of Vehicle/Driver Performance Using Genetic Algorithms

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.