Autonomous Car Simulation Using Evolutionary Neural Network Algorithm

Automation with artificial intelligence (AI) has widely implemented in robotics, transportation and manufacture. AI has become a powerful technology that change human life and help human more flexible doing something. In this paper, it will show a result of simulation from an autonomous car using the evolutionary neural network algorithm which combines genetic algorithm and neural network. The purpose of the simulation is to test the model that we develop to know the right direction based on the track, so the evolutionary neural network that implemented to the autonomous car be able to deliver the best solution before it implements in the real machine or car technology. Genetic algorithm combines with a neural network to reach an evolution condition. The evolution process is achieved through crossover, mutation and selection process, so the algorithm will give the best result from the iteration of the experiment. The result of our experiment shows that evolutionary neural network algorithm give the best result within 3 layer architecture, with iteration average is 14.5 reach finish point (check point) 3 in the track simulation. Based on the simulation, our car model can find out the right direction.