Self-driving a Car in Simulation Through a CNN

This work presents a comparison between different Convolutional Neural Network models, testing its performance when it leads a self-driving car in a simulated environment. To do so, driving data has been obtained manually driving the simulator as ground truth and different network models with diverse complexity levels has been created and trained with the data previously obtained using end-to-end deep learning techniques. Once this CNNs are trained, they are tested in the driving simulator, checking their ability of minimizing the car distance to the center of the lane, its heading error and its RMSE. The neural networks will be evaluated according to these parameters. Finally, conclusions will be drawn about the performance of the different models according to the parameters mentioned before in order to find the optimum CNN for the developed application.