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Professional race-car drivers can execute extreme overtaking maneuvers.
D. A. Pomerleau, “Alvinn: An autonomous land vehicle in a neural network,” in Advances in neural information processing systems , 1989, pp. 305–313
1989
Earlier work this paper cites.
D. A. Pomerleau, “Efficient training of artificial neural networks for autonomous navigation,” Neural computation , vol. 3, no. 1, pp. 88–97, 1991
1991
Earlier work this paper cites.
T. Hellstrom and O. Ringdahl, “Follow the past: a path-tracking algorithm for autonomous vehicles,” International journal of vehicle autonomous systems , vol. 4, no. 2-4, pp. 216–224, 2006
2006
Earlier work this paper cites.
Y. Kuwata, G. A. Fiore, J. Teo, E. Frazzoli, and J. P. How, “Motion planning for urban driving using rrt,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2008, pp. 1681–1686
2008
Earlier work this paper cites.
D. Loiacono, A. Prete, P. L. Lanzi, and L. Cardamone, “Learning to overtake in torcs using simple reinforcement learning,” in IEEE Congress on Evolutionary Computation . IEEE, 2010, pp. 1–8
2010
Earlier work this paper cites.
S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics , 2011, pp. 627–635
2011
Earlier work this paper cites.
L. Ma, J. Xue, K. Kawabata, J. Zhu, C. Ma, and N. Zheng, “A fast rrt algorithm for motion planning of autonomous road vehicles,” in 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2014, pp. 1033–1038
2014
Earlier work this paper cites.
P. Petrov and F. Nashashibi, “Modeling and nonlinear adaptive control for autonomous vehicle overtaking,” IEEE Transactions on Intelligent Transportation Systems , vol. 15, no. 4, pp. 1643–1656, 2014
2014
Earlier work this paper cites.
R. Verschueren, S. De Bruyne, M. Zanon, J. V. Frasch, and M. Diehl, “Towards time-optimal race car driving using nonlinear mpc in real-time,” in 53rd IEEE conference on decision and control . IEEE, 2014, pp. 2505–2510
2014
Earlier work this paper cites.
X. Li, X. Xu, and L. Zuo, “Reinforcement learning based overtaking decision-making for highway autonomous driving,” in 2015 Sixth International Conference on Intelligent Control and Information Processing (ICICIP) , 2015, pp. 336–342
2015
Cited alongside, same era.
B. Paden, M. Čáp, S. Z. Yong, D. Yershov, and E. Frazzoli, “A survey of motion planning and control techniques for self-driving urban vehicles,” IEEE Transactions on intelligent vehicles , vol. 1, no. 1, pp. 33–55, 2016
2016
Cited alongside, same era.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” in Advances in neural information processing systems , 2016, pp. 4565–4573
2016
Cited alongside, same era.
A. Buyval, A. Gabdulin, R. Mustafin, and I. Shimchik, “Deriving overtaking strategy from nonlinear model predictive control for a race car,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 2623–2628
2017
Cited alongside, same era.
S. Dixit, U. Montanaro, M. Dianati, D. Oxtoby, T. Mizutani, A. Mouzakitis, and S. Fallah, “Trajectory planning for autonomous high-speed overtaking in structured environments using robust mpc,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 6, pp. 2310–2323, 2019
2019
Later among the works it cites.
M. Wang, Z. Wang, J. Talbot, J. C. Gerdes, and M. Schwager, “Game theoretic planning for self-driving cars in competitive scenarios,” in Robotics: Science and Systems , 2019
2019
Later among the works it cites.
J. Ni, J. Hu, and C. Xiang, “Robust path following control at driving/handling limits of an autonomous electric racecar,” IEEE Transactions on Vehicular Technology , vol. 68, no. 6, pp. 5518–5526, 2019
2019
Later among the works it cites.
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
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S. Dixit, S. Fallah, U. Montanaro, M. Dianati, A. Stevens, F. Mccullough, and A. Mouzakitis, “Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects,” Annual Reviews in Control , vol. 45, pp. 76–86, 2018
2018
Cited alongside, same era.
W. Farag and Z. Saleh, “Behavior cloning for autonomous driving using convolutional neural networks,” in 2018 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) . IEEE, 2018, pp. 1–7
2018
Cited alongside, same era.
J. Achiam, “Spinning Up in Deep Reinforcement Learning,” 2018
2018
Cited alongside, same era.
S. Fujimoto, H. Hoof, and D. Meger, “Addressing function approximation error in actor-critic methods,” in International Conference on Machine Learning . PMLR, 2018, pp. 1587–1596
2018
Cited alongside, same era.
A. Heilmeier, A. Wischnewski, L. Hermansdorfer, J. Betz, M. Lienkamp, and B. Lohmann, “Minimum curvature trajectory planning and control for an autonomous race car,” Vehicle System Dynamics , pp. 1–31, 2019
2019
Cited alongside, same era.
Later among the works it cites.
J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics , vol. 5, no. 47, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
W. Schwarting, T. Seyde, I. Gilitschenski, L. Liebenwein, R. Sander, S. Karaman, and D. Rus, “Deep latent competition: Learning to race using visual control policies in latent space,” in Conference on Robot Learning , 2020
2020
Later among the works it cites.
S. Fujimoto, D. Meger, and D. Precup, “Off-policy deep reinforcement learning without exploration,” in International Conference on Machine Learning , 2019, pp. 2052–2062
2062
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M. Jaritz, R. De Charette, M. Toromanoff, E. Perot, and F. Nashashibi, “End-to-end race driving with deep reinforcement learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 2070–2075
2075
Closest in time.