Autonomous vehicle driving systems face the challenge of providing safe, feasible and human-like driving policies. The traditional modular approach involves a search-based or optimization-based motion planning followed by a feedback model-based controller. This may prove to be inadequate due to model uncertainties, limited computation time and difficulties in incorporating personalized and natural behaviour. The more recent end-to-end approach aims at overcoming these issues by learning from real drivers' data a policy to map from sensor data to controls using deep learning. Although being attractive by its simplicity, it also shows some drawbacks such as sample inefficiency and difficulties in validation and interpretability.