In this work we investigate the performance of different deep learning methods in the task of self driving, aided by regularising methods and synthetic data augmentation. We obtain results for models/methods including vanilla Multi layer preceptors, vanilla Convolutional neural networks as well as results using Transfer Learning with MobileNetV3Small and EfficentNetV2 B0 and B1, with performances ranking in the respective order from worse to best. We obtain a best performing theoretical model using the EfficentNetV2B0 model, obtaining a Kaggle MSE score of 0.01161. Fur- thermore on the practical testing, we used the MobvileNetV3Small and obtained a poorly performing model, only scoring 9/35 points in the live testing.
