Evaluation of Vehicle/Driver Performance Using Genetic Algorithms

Abstract

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 geneticalgorithm-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.

Evolving a fuzzy controller for a Car Racing Competition

“…The techniques and the technologies supporting Automatic Vehicle Guidance (AVG) are an important issue [1]. Automobile manufacturers see automatic driving as a very interesting product with motivating key features which allow improving car safety, reduction of emissions and fuel consumption or optimising of driver comfort during long journeys.…”

Evolving a rule system controller for automatic driving in a car racing competition

“…• Track: the set of sensors that indicates the distance to the track edges. Only three of them have been used (the front one and the immediate sensors on its right and left) and they have been discretized to a unique range [0,2]. In this case, 0 means that the track edge has been detected between 20 and 100 meters, 1 means that the track edge is up to 20 meters and 2 that no track edge is seen.…”

“…The techniques and the technologies supporting Automatic Vehicle Guidance (AVG) are an important issue [2]. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improving of the car safety, reduction in emission or fuel consumption or optimizing of the driver comfort during long journeys.…”

The WCCI 2008 simulated car racing competition

“…The usage of sensors to obtain autonomous driving has been addressed by numerous researchers ( [8], [9], [10], [11]), as well as the use of evolutionary algorithms in this field ( [12], [13], [14]). …”