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Autonomous driving has achieved significant progress in recent years, but autonomous cars are still unable to tackle high-risk situations where a potential accident is likely.
Alvinn: An autonomous land vehicle in a neural network
Dean A Pomerleau · 1989
Earlier work this paper cites.
Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton · 1993
Earlier work this paper cites.
Apprenticeship learning via inverse reinforcement learning
Pieter Abbeel and Andrew Y Ng · 2004
Earlier work this paper cites.
Off-road obstacle avoidance through end-to-end learning
Urs Muller, Jan Ben, Eric Cosatto, Beat Flepp, and Yann L Cun · 2006
Earlier work this paper cites.
Autonomous driving in urban environments: Boss and the urban challenge
Chris Urmson, Joshua Anhalt, Drew Bagnell, Christopher Baker, Robert Bittner, MN Clark, John Dolan, Dave Duggins, Tugrul Galatali, Chris Geyer, et al · 2008
Earlier work this paper cites.
Junior: The stanford entry in the urban challenge
Michael Montemerlo, Jan Becker, Suhrid Bhat, Hendrik Dahlkamp, Dmitri Dolgov, Scott Ettinger, Dirk Haehnel, Tim Hilden, Gabe Hoffmann, Burkhard Huhnke, et al · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
Earlier work this paper cites.
Optimal policy switching algorithms for reinforcement learning
Gheorghe Comanici and Doina Precup · 2010
Earlier work this paper cites.
No-regret reductions for imitation learning and structured prediction
Stéphane Ross, Geoffrey J Gordon, and J Andrew Bagnell · 2011
Earlier work this paper cites.
Hierarchical reinforcement learning with movement primitives
Freek Stulp and Stefan Schaal · 2011
Earlier work this paper cites.
Continuous inverse optimal control with locally optimal examples
Sergey Levine and Vladlen Koltun · 2012
Earlier work this paper cites.
Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
Earlier work this paper cites.
Planning for autonomous cars that leverage effects on human actions
Dorsa Sadigh, S. Shankar Sastry, Sanjit A. Seshia, and Anca D. Dragan · 2016
Earlier work this paper cites.
Information gathering actions over human internal state
Dorsa Sadigh, S. Shankar Sastry, Sanjit A. Seshia, and Anca Dragan · 2016
Earlier work this paper cites.
A survey of motion planning and control techniques for self-driving urban vehicles
Brian Paden, Michal Čáp, Sze Zheng Yong, Dmitry Yershov, and Emilio Frazzoli · 2016
Earlier work this paper cites.
Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
Earlier work this paper cites.
Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
Earlier work this paper cites.
Safe, multi-agent, reinforcement learning for autonomous driving
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2016
Cited alongside, same era.
Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, and Josh Tenenbaum · 2016
Cited alongside, same era.
Desire: Distant future prediction in dynamic scenes with interacting agents
Namhoon Lee, Wongun Choi, Paul Vernaza, Christopher B Choy, Philip HS Torr, and Manmohan Chandraker · 2017
Cited alongside, same era.
Large-scale cost function learning for path planning using deep inverse reinforcement learning
Markus Wulfmeier, Dushyant Rao, Dominic Zeng Wang, Peter Ondruska, and Ingmar Posner · 2017
Cited alongside, same era.
Deep reinforcement learning framework for autonomous driving
Ahmad EL Sallab, Mohammed Abdou, Etienne Perot, and Senthil Yogamani · 2017
Cited alongside, same era.
Planning and decision-making for autonomous vehicles
Wilko Schwarting, Javier Alonso-Mora, and Daniela Rus · 2018
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Multi-agent generative adversarial imitation learning
Jiaming Song, Hongyu Ren, Dorsa Sadigh, and Stefano Ermon · 2018
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Hierarchical imitation and reinforcement learning
Hoang M Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, and Hal Daumé III · 2018
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Overcoming exploration in reinforcement learning with demonstrations
Ashvin Nair, Bob McGrew, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2018
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Scalable end-to-end autonomous vehicle testing via rare-event simulation
Matthew O’Kelly, Aman Sinha, Hongseok Namkoong, Russ Tedrake, and John C Duchi · 2018
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Query-efficient imitation learning for end-to-end simulated driving
Jiakai Zhang and Kyunghyun Cho · 2017
Cited alongside, same era.
Explaining how a deep neural network trained with end-to-end learning steers a car
Mariusz Bojarski, Philip Yeres, Anna Choromanska, Krzysztof Choromanski, Bernhard Firner, Lawrence Jackel, and Urs Muller · 2017
Cited alongside, same era.
Imitating driver behavior with generative adversarial networks
Alex Kuefler, Jeremy Morton, Tim Wheeler, and Mykel Kochenderfer · 2017
Cited alongside, same era.
Virtual to real reinforcement learning for autonomous driving
Xinlei Pan, Yurong You, Ziyan Wang, and Cewu Lu · 2017
Cited alongside, same era.
Feudal networks for hierarchical reinforcement learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
Active preference-based learning of reward functions
Dorsa Sadigh, Anca D. Dragan, S. Shankar Sastry, and Sanjit A. Seshia · 2017
Cited alongside, same era.
Alexander Amini, Guy Rosman, Sertac Karaman, and Daniela Rus · 2019
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Exploring the limitations of behavior cloning for autonomous driving
Felipe Codevilla, Eder Santana, Antonio M López, and Adrien Gaidon · 2019
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Active learning of reward dynamics from hierarchical queries
Chandrayee Basu, Erdem Biyik, Zhixun He, Mukesh Singhal, and Dorsa Sadigh · 2019
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A deep reinforcement learning driving policy for autonomous road vehicles
Konstantinos Makantasis, Maria Kontorinaki, and Ioannis Nikolos · 2019
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Uncertainty-aware driver trajectory prediction at urban intersections
Xin Huang, Stephen G McGill, Brian C Williams, Luke Fletcher, and Guy Rosman · 2019
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Model-free deep reinforcement learning for urban autonomous driving
Jianyu Chen, Bodi Yuan, and Masayoshi Tomizuka · 2019
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Deep reinforcement learning with external control: self-driving car application
Fenjiro Youssef and Benbrahim Houda · 2019
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Learning when to drive in intersections by combining reinforcement learning and model predictive control
Tommy Tram, Ivo Batkovic, Mohammad Ali, and Jonas Sjöberg · 2019
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Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Combining learned skills and reinforcement learning for robotic manipulations
Robin Strudel, Alexander Pashevich, Igor Kalevatykh, Ivan Laptev, Josef Sivic, and Cordelia Schmid · 2019
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When humans aren’t optimal: Robots that collaborate with risk-aware humans
Minae Kwon, Erdem Biyik, Aditi Talati, Karan Bhasin, Dylan P. Losey, and Dorsa Sadigh · 2020
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Model primitives for hierarchical lifelong reinforcement learning
Bohan Wu, Jayesh K Gupta, and Mykel Kochenderfer · 2020
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Composing task-agnostic policies with deep reinforcement learning
Ahmed H. Qureshi, Jacob J. Johnson, Yuzhe Qin, Taylor Henderson, Byron Boots, and Michael C. Yip · 2020
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