Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective

C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in

2015

Earlier work this paper cites.

L. Tai, S. Li, and M. Liu, “A deep-network solution towards model-less obstacle avoidance,” in

2016

Earlier work this paper cites.

A. Giusti, J. Guzzi, D. C. Cireşan, F. He, J. P. Rodríguez, F. Fontana, M. Faessler, C. Forster, J. Schmidhuber, G. D. Caro, D. Scaramuzza, and L. M. Gambardella, “A machine learning approach to visual perception of forest trails for mobile robots,”

2016

Earlier work this paper cites.

Y. Gal, “Uncertainty in deep learning,” Ph.D. dissertation, University of Cambridge, 2016

2016

Earlier work this paper cites.

A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in

2017

Earlier work this paper cites.

J. Zhang, J. T. Springenberg, J. Boedecker, and W. Burgard, “Deep reinforcement learning with successor features for navigation across similar environments,” in

2017

Earlier work this paper cites.

A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” in

2017

Earlier work this paper cites.

G. Kahn, A. Villaflor, V. Pong, P. Abbeel, and S. Levine, “Uncertainty-aware reinforcement learning for collision avoidance,”

Original

2017

Earlier work this paper cites.

X. Pan, Y. You, Z. Wang, and C. Lu, “Virtual to real reinforcement learning for autonomous driving,” in

2017

Cited alongside, same era.

J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in

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

Cited alongside, same era.

L. Yang, X. Liang, T. Wang, and E. Xing, “Real-to-virtual domain unification for end-to-end autonomous driving,” in

2018

Cited alongside, same era.

X. Liang, T. Wang, L. Yang, and E. Xing, “Cirl: Controllable imitative reinforcement learning for vision-based self-driving,” in

2018

Cited alongside, same era.

S. Choi, K. Lee, S. Lim, and S. Oh, “Uncertainty-aware learning from demonstration using mixture density networks with sampling-free variance modeling,” in

2018

Later among the works it cites.

M. Mueller, A. Dosovitskiy, B. Ghanem, and V. Koltun, “Driving policy transfer via modularity and abstraction,” in

2018

Later among the works it cites.

X. Huang, M.-Y. Liu, S. Belongie, and J. Kautz, “Multimodal unsupervised image-to-image translation,” in

2018

Later among the works it cites.

H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. Singh, and M.-H. Yang, “Diverse image-to-image translation via disentangled representations,” in

2018

Later among the works it cites.

A. Almahairi, S. Rajeswar, A. Sordoni, P. Bachman, and A. Courville, “Augmented cyclegan: Learning many-to-many mappings from unpaired data,”

Original

2018