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Imitation learning by behavioral cloning is a prevalent method that has achieved some success in vision-based autonomous driving.
B. Wymann, E. Espié, C. Guionneau, C. Dimitrakakis, R. Coulom, and A. Sumner, “Torcs, the open racing car simulator,” Software available at http://torcs. sourceforge. net , vol. 4, p. 6, 2000
2000
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U. Muller, J. Ben, E. Cosatto, B. Flepp, and Y. L. Cun, “Off-road obstacle avoidance through end-to-end learning,” in Advances in neural information processing systems , 2006, pp. 739–746
2006
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
Earlier work this paper cites.
2016
Earlier work this paper cites.
A. Kendall and R. Cipolla, “Modelling uncertainty in deep learning for camera relocalization,” in 2016 IEEE international conference on Robotics and Automation (ICRA) . IEEE, 2016, pp. 4762–4769
2016
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning , 2016, pp. 1050–1059
2016
Earlier work this paper cites.
Y. Yamani, P. Bıçaksız, D. B. Palmer, J. M. Cronauer, and S. Samuel, “Following expert’s eyes: Evaluation of the effectiveness of a gaze-based training intervention on young drivers’ latent hazard anticipation skills,” in 9th International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design , 2017
2017
Cited alongside, same era.
R. McAllister, Y. Gal, A. Kendall, M. van der Wilk, A. Shah, R. Cipolla, and A. Weller, “Concrete problems for autonomous vehicle safety: Advantages of bayesian deep learning,” in Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI-17 , 2017, pp. 4745–4753. [Online]. Available: https://doi.org/10.24963/ijcai.2017/661
2017
Cited alongside, same era.
2017
Cited alongside, same era.
F. Codevilla, M. Miiller, A. López, V. Koltun, and A. Dosovitskiy, “End-to-end driving via conditional imitation learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1–9
2018
Later among the works it cites.
2018
Later among the works it cites.
C. Liu, Y. Chen, L. Tai, H. Ye, M. Liu, and B. E. Shi, “A gaze model improves autonomous driving,” in Proceedings of the 2019 ACM Symposium on Eye Tracking Research & Applications . ACM, 2019, to appear
2019
Closest in time.
J. Zhang, L. Tai, P. Yun, Y. Xiong, M. Liu, J. Boedecker, and W. Burgard, “Vr-goggles for robots: Real-to-sim domain adaptation for visual control,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 1148–1155, 2019
2019
Closest in time.
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2017
Cited alongside, same era.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . IEEE, 2017, pp. 5967–5976
2017
Cited alongside, same era.
X. Liang, T. Wang, L. Yang, and E. Xing, “Cirl: Controllable imitative reinforcement learning for vision-based self-driving,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 584–599
2018
Cited alongside, same era.
A. G. Kendall, “Geometry and uncertainty in deep learning for computer vision,” Ph.D. dissertation, University of Cambridge, 2019
2019
Closest in time.