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This paper presents a safe reinforcement learning system for automated driving that benefits from multimodal future trajectory predictions.
Mixture density networks
Christopher M Bishop · 1994
Earlier work this paper cites.
Introduction to reinforcement learning
Richard S Sutton, Andrew G Barto, et al · 1998
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Generating sequences with recurrent neural networks
Alex Graves · 2013
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Earlier work this paper cites.
Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
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Hado Van Hasselt, Arthur Guez, and David Silver · 2016
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On a formal model of safe and scalable self-driving cars
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2017
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An autoregressive recurrent mixture density network for parametric speech synthesis
Xin Wang, Shinji Takaki, and Junichi Yamagishi · 2017
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Deep Q-learning with dynamically-learned safety module: A case study in autonomous driving
Ali Baheri, Subramanya Nageshrao, Ilya Kolmanovsky, Anouck Girard, H Eric Tseng, and Dimitar Filev
Cited in the paper.
Recurrent world models facilitate policy evolution
David Ha and Jürgen Schmidhuber · 2018
Later among the works it cites.
Deep reinforcement learning with enhanced safety for autonomous highway driving
Ali Baheri, Subramanya Nageshrao, H Eric Tseng, Ilya Kolmanovsky, Anouck Girard, and Dimitar Filev · 2019
Later among the works it cites.
Vision-based autonomous driving: A model learning approach
Ali Baheri, Ilya Kolmanovsky, Anouck Girard, H Eric Tseng, and Dimitar Filev · 2020
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