Abstract
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%.
Highlights
Simulation is a powerful approach in educating people who are not acquainted with the real physical environment
This study presents a mechanism which incorporates the use of artificial neural networks in simulations of several different cars with different properties successfully
The results show that the predictions made by the Artificial Neural Networks (ANNs) can safely replace the physics engine which has been simulated
Summary
Simulation is a powerful approach in educating people who are not acquainted with the real physical environment. Due to its commercial nature it is hard to see any publications revealing the details of this matter Using this approach, it is possible to use the simulation tools that help the pilots to be trained without endangering the planes. For the sake of realism, a car simulation program must use a steering wheel and pedals hardware. The physics engine is a function which performs the physical principles and car dynamics on the data flow from the steering wheel and pedals hardware and generates outputs like speed, engine rotation per minute and advanced distance. The input data was supplied by the pedals hardware, which originally depends on the specifications of the simulated cars and the output is the speed, rotation of the engine in rpm, and the traveled distance based on x and y values. To calculate rigid body dynamics common formulas given in [1] has been used
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