• A. Amini, W. Schwarting, G. Rosman, B. Araki, S. Karaman, and D. Rus, “Variational autoencoder for end-to-end control of
autonomous driving with novelty detection and training de-biasing,” in IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS). IEEE, 2018.
• M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang,
et al., “End to end learning for self-driving cars,” arXiv preprint arXiv:1604.07316, 2016.
• F. Codevilla, M. Mller, A. Dosovitskiy, A. Lopez, and V. Koltun, “End-to-end driving via conditional imitation learning,”
arXiv preprint arXiv:1710.02410, 2017.
• D. A. Pomerleau, “ALVINN: An autonomous land vehicle in a neural network,” in Advances in neural information
processing systems, 1989, pp. 305–313.
• M. Bansal , A. Krizhevsky, and A. Ogale, “ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the
Worst,” arXiv preprint arXiv: 1812.03079, 2018.
• A. Sax, B. Emi, A. R. Zamir, L. J. Guibas, S. Savarese, and J. Malik, “Mid-level visual representations improve
generalization and sample efficiency for learning active tasks,” arXiv preprint arXiv:1812.11971, 2018.