The Use of Artificial Neural Networks in Simulation of Mobile Ground Vehicles

In this study, we have developed a platform which incorporates Artificial Neural Networks (ANNs) in simulating body dynamics of mobile ground vehicles (e.g. cars). This is a part of our research project in which we plan to provide a platform for educating the driver candidates in virtual environments: where the drivers can be educated fully in “Artificial Cities”. To start with, 6 different makes of cars with different engine properties has been simulated with the appropriate data provided by the manufacturers and rules of physics. A joystick steering wheel has been used to produce the necessary inputs for the ANN based physics engine. To train the network, Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function have been used. The statistical error levels are negligible. The Absolute Fraction of Variance (R2) values for both the training and test data are about 99.999% and the mean error value for both data group is lesser than 0.5%

Population Based Procedural Artificial City Generation Using Beta Distribution

Artificial city generation on computer graphics platforms introduce several problems from the point of view of the application programmer. Especially in the cases where the product is aimed for virtual reality applications, this becomes more important since the target is achieving city layouts akin to the real cities. The same is valid for the civil engineers where the layouts of the blocks/cities are determined in advance of the construction. An important parameter for artificial cities is the determination of the population distribution over the cities which in turn affect the overall appearance of the city or the blocks forming it. In this study, the Beta distribution has been used to disperse artificial city populations over the city blocks to generate cities that do not look to regular. The system uses HTF based maps and the produced 3D cities are quite realistic when compared to the similar products