Fetching the paper…
Reading the bibliography…
End-to-end autonomous driving provides a feasible way to automatically maximize overall driving system performance by directly mapping the raw pixels from a front-facing camera to control signals.
Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, and L. D. Jackel, “Backpropagation applied to handwritten zip code recognition,” Neural computation , vol. 1, no. 4, pp. 541–551, 1989
1989
Earlier work this paper cites.
J. M. Joyce, “Kullback-leibler divergence,” in International encyclopedia of statistical science . Springer, 2011, pp. 720–722
2011
Earlier work this paper cites.
2014
Earlier work this paper cites.
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski et al. , “Human-level control through deep reinforcement learning,” nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. Levine, C. Finn, T. Darrell, and P. Abbeel, “End-to-end training of deep visuomotor policies,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 1334–1373, 2016
2016
Earlier work this paper cites.
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot et al. , “Mastering the game of go with deep neural networks and tree search,” nature , vol. 529, no. 7587, pp. 484–489, 2016
2016
Earlier work this paper cites.
D.-A. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units (elus),” in International Conference on Learning Representations , 2016
2016
Earlier work this paper cites.
C. Hubmann, M. Becker, D. Althoff, D. Lenz, and C. Stiller, “Decision making for autonomous driving considering interaction and uncertain prediction of surrounding vehicles,” in 2017 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2017, pp. 1671–1678
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton et al. , “Mastering the game of go without human knowledge,” nature , vol. 550, no. 7676, pp. 354–359, 2017
2017
Earlier work this paper cites.
P. Wolf, C. Hubschneider, M. Weber, A. Bauer, J. Härtl, F. Dürr, and J. M. Zöllner, “Learning how to drive in a real world simulation with deep q-networks,” in 2017 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2017, pp. 244–250
2017
Earlier work this paper cites.
J. Kim and J. Canny, “Interpretable learning for self-driving cars by visualizing causal attention,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2942–2950
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16
2017
Cited alongside, same era.
D. Neven, B. De Brabandere, S. Georgoulis, M. Proesmans, and L. Van Gool, “Towards end-to-end lane detection: an instance segmentation approach,” in 2018 IEEE intelligent vehicles symposium (IV) . IEEE, 2018, pp. 286–291
2018
Cited alongside, same era.
F. Codevilla, M. Müller, 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. 4693–4700
2018
Cited alongside, same era.
2018
Cited alongside, same era.
E. Yurtsever, L. Capito, K. Redmill, and U. Ozgune, “Integrating deep reinforcement learning with model-based path planners for automated driving,” in 2020 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2020, pp. 1311–1316
2020
Later among the works it cites.
M. Okada, N. Kosaka, and T. Taniguchi, “Planet of the bayesians: Reconsidering and improving deep planning network by incorporating bayesian inference,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 5611–5618
2020
Later among the works it cites.
A. X. Lee, A. Nagabandi, P. Abbeel, and S. Levine, “Stochastic latent actor-critic: Deep reinforcement learning with a latent variable model,” Advances in Neural Information Processing Systems , vol. 33, pp. 741–752, 2020
2020
Later among the works it cites.
G. Zhu, M. Zhang, H. Lee, and C. Zhang, “Bridging imagination and reality for model-based deep reinforcement learning,” Advances in Neural Information Processing Systems , vol. 33, pp. 8993–9006, 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, “Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor,” in International conference on machine learning . PMLR, 2018, pp. 1861–1870
2018
Cited alongside, same era.
D. Ha and J. Schmidhuber, “Recurrent world models facilitate policy evolution,” Advances in neural information processing systems , vol. 31, 2018
2018
Cited alongside, same era.
Y. Hou, Z. Ma, C. Liu, and C. C. Loy, “Learning lightweight lane detection cnns by self attention distillation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 1013–1021
2019
Cited alongside, same era.
K. Okamoto and P. Tsiotras, “Optimal stochastic vehicle path planning using covariance steering,” IEEE Robotics and Automation Letters , vol. 4, no. 3, pp. 2276–2281, 2019
2019
Cited alongside, same era.
J. Chen, W. Zhan, and M. Tomizuka, “Autonomous driving motion planning with constrained iterative lqr,” IEEE Transactions on Intelligent Vehicles , vol. 4, no. 2, pp. 244–254, 2019
2019
Cited alongside, same era.
J. Chen, B. Yuan, and M. Tomizuka, “Model-free deep reinforcement learning for urban autonomous driving,” in 2019 IEEE intelligent transportation systems conference (ITSC) . IEEE, 2019, pp. 2765–2771
2019
Cited alongside, same era.
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson, “Learning latent dynamics for planning from pixels,” in International conference on machine learning . PMLR, 2019, pp. 2555–2565
2019
Cited alongside, same era.
Y. Xiao, F. Codevilla, A. Gurram, O. Urfalioglu, and A. M. López, “Multimodal end-to-end autonomous driving,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 1, pp. 537–547, 2020
2020
Cited alongside, same era.
2020
Later among the works it cites.
2021
Later among the works it cites.
D. Hafner, T. P. Lillicrap, M. Norouzi, and J. Ba, “Mastering atari with discrete world models,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
J. Duan, Y. Guan, S. E. Li, Y. Ren, Q. Sun, and B. Cheng, “Distributional soft actor-critic: Off-policy reinforcement learning for addressing value estimation errors,” IEEE transactions on neural networks and learning systems , vol. 33, no. 11, pp. 6584–6598, 2021
2021
Later among the works it cites.
H. Ma, J. Chen, S. Eben, Z. Lin, Y. Guan, Y. Ren, and S. Zheng, “Model-based constrained reinforcement learning using generalized control barrier function,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 4552–4559
2021
Later among the works it cites.
J. Chen, S. E. Li, and M. Tomizuka, “Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning,” IEEE Transactions on Intelligent Transportation Systems , vol. 23, no. 6, pp. 5068–5078, 2022
2022
Closest in time.
Y. Guan, Y. Ren, Q. Sun, S. E. Li, H. Ma, J. Duan, Y. Dai, and B. Cheng, “Integrated decision and control: toward interpretable and computationally efficient driving intelligence,” IEEE transactions on cybernetics , vol. 53, no. 2, pp. 859–873, 2022
2022
Closest in time.
J. Duan, J. Li, Q. Ge, S. E. Li, M. Bujarbaruah, F. Ma, and D. Zhang, “Relaxed actor-critic with convergence guarantees for continuous-time optimal control of nonlinear systems,” IEEE Transactions on Intelligent Vehicles , 2023
2023
Closest in time.
J. Wu, Z. Huang, and C. Lv, “Uncertainty-aware model-based reinforcement learning: Methodology and application in autonomous driving,” IEEE Transactions on Intelligent Vehicles , vol. 8, no. 1, pp. 194–203, 2023
2023
Closest in time.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in International Conference on Learning Representations , 2019
2023
Closest in time.