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Urban intersections are prone to delays and inefficiencies due to static precedence rules and occlusions limiting the view on prioritized traffic.
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S. Fujimoto, H. van Hoof, and D. Meger, “Addressing Function Approximation Error in Actor-Critic Methods,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. PMLR, Jul. 2018, pp. 1587–1596
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2020
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M. Huegle, G. Kalweit, M. Werling, and J. Boedecker, “Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Driving,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, May 2020, pp. 4329–4335
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P. Hart and A. Knoll, “Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments,” in 2020 IEEE Intelligent Vehicles Symposium (IV) . IEEE, Oct. 2020, pp. 1589–1594
2020
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M. Buchholz, J. C. Müller, M. Herrmann, J. Strohbeck, B. Völz, M. Maier, J. Paczia, O. Stein, H. Rehborn, and R.-W. Henn, “Handling Occlusions in Automated Driving Using a Multiaccess Edge Computing Server-Based Environment Model From Infrastructure Sensors,” IEEE Intelligent Transportation Systems Magazine , to appear, doi: 10.1109/MITS.2021.3089743
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2018
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J. Chen, B. Yuan, and M. Tomizuka, “Deep Imitation Learning for Autonomous Driving in Generic Urban Scenarios with Enhanced Safety,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, Nov. 2019, pp. 2884–2890
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M. Bansal, A. Krizhevsky, and A. Ogale, “ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst,” in Robotics: Science and Systems . Robotics: Science and Systems Foundation, 2019
2019
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Y. Wu, H. Chen, and F. Zhu, “DCL-AIM: Decentralized Coordination Learning of Autonomous Intersection Management for Connected and Automated Vehicles,” Transportation Research Part C: Emerging Technologies , vol. 103, pp. 246–260, Jun. 2019
2019
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M. Huegle, G. Kalweit, B. Mirchevska, M. Werling, and J. Boedecker, “Dynamic Input for Deep Reinforcement Learning in Autonomous Driving,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, Nov. 2019, pp. 7566–7573
2019
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M. Fey and J. E. Lenssen, “Fast Graph Representation Learning with PyTorch Geometric,” May 2019. [Online]. Available: https://github.com/pyg-team/pytorch_geometric
2019
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2021
Later among the works it cites.
Z. Zhu and H. Zhao, “A Survey of Deep RL and IL for Autonomous Driving Policy Learning,” IEEE Transactions on Intelligent Transportation Systems , to be published, doi: 10.1109/TITS.2021.3134702
2021
Later among the works it cites.
A. P. Capasso, P. Maramotti, A. Dell’Eva, and A. Broggi, “End-to-End Intersection Handling using Multi-Agent Deep Reinforcement Learning,” in 2021 IEEE Intelligent Vehicles Symposium (IV) , 2021, pp. 443–450
2021
Later among the works it cites.
S. Gronauer and K. Diepold, “Multi-Agent Deep Reinforcement Learning: A Survey,” Artificial Intelligence Review , Apr. 2021
2021
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M. B. Mertens, J. Müller, and M. Buchholz, “Cooperative Maneuver Planning for Mixed Traffic at Unsignalized Intersections Using Probabilistic Predictions,” in 2022 IEEE Intelligent Vehicles Symposium (IV) , 2022, to appear
2022
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