In this paper we introduce a visualization application for a vehicle simulation with an automatic pilot. The application is written in OpenGL and includes a model of a vehicle (a motorcycle) based on the physical laws of motion, including kinematics, gravitation, and friction. The vehicle can be controlled by the user through keyboard and mouse commands, as well as by an automatic pilot. The latter is implemented as a multi-agent probabilistic scheme using perceptual data.Our intention is to simulate the behavior of a human driver on the road. The performance of the automatic pilot is compared with that of a human player.
In Gennany and elsewhere, discussion of alternative methods of urban stonnwater management have led to the belief that an economically and ecologically sound combination of central and distributed methods will be the concept of the future. However introduction of methods other than traditional combined sewer systems has been limited by the shortage of planning tools and technologies. Adding a number of frequently used better stonnwater management practices (BSMPs) has widely extended the use of an existing stonnwater water balance and pollution load model.
In this paper we present an application of genetic algorithms to an autonomous pilot designed for motorized single-track vehicles (motorcycles). The pilot is implemented as a multi-agent application using a physical model of the motorcycle and is embedded in an interactive application. We compare the performance of the configuration obtained by genetic algorithms with the manual configuration of the pilot and with the performance of human players.The main contribution of the paper is proposing a model for piloting a single-track vehicle based only on perceptual information such as it would be observed by a human in the case of a real vehicle, and showing how the genetic algorithms can contribute efficiently to configuring the autonomous pilot.
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