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Training self-driving systems to be robust to the long-tail of driving scenarios is a critical problem.
Model-predictive policy learning with uncertainty regularization for driving in dense traffic
Mikael Henaff, Alfredo Canziani, and Yann LeCun · 1901
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Model-based reinforcement learning for atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H. Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, and Henryk Michalewski · 1903
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Kavosh Asadi, Dipendra Misra, Seungchan Kim, and Michel L. Littman · 1905
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Arthur E. Bryson, Yu-Chi Ho, and George M. Siouris · 1979
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Algorithms for inverse reinforcement learning
Andrew Y. Ng and Stuart Russell · 2000
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Trajectron++: Multi-agent generative trajectory forecasting with heterogeneous data for control
Tim Salzmann, Boris Ivanovic, Punarjay Chakravarty, and Marco Pavone · 2001
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Dynamic Programming and Optimal Control , volume I
Dimitri P. Bertsekas · 2005
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Ngsim interstate 80 freeway dataset, 2006
John Halkias and James Colyar · 2006
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Planning Algorithms
S. M. LaValle · 2006
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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, Doug Johnston, Stefan Klumpp, Dirk Langer, Anthony Levandowski, Jesse Levinson, Julien Marcil, David Orenstein, Johannes Paefgen, Isaac Penny, Anna Petrovskaya, Mike Pflueger, Ganymed Stanek, David Stavens, Antone Vogt, and Sebastian Thrun · 2008
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Navigating car-like robots in unstructured environments using an obstacle sensitive cost function
J. Ziegler, Moritz Werling, and Joachim Schroder · 2008
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Generative adversarial networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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The scenario approach for stochastic model predictive control with bounds on closed-loop constraint violations
Georg Schildbach, Lorenzo Fagiano, Christoph Frei, and Manfred Morari · 2014
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Kinematic and dynamic vehicle models for autonomous driving control design
Jason Kong, Mark Pfeiffer, Georg Schildbach, and Francesco Borrelli · 2015
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Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard Lewis, and Satinder Singh · 2015
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A survey of motion planning and control techniques for self-driving urban vehicles
Stochastic video generation with a learned prior
Emily Denton and Rob Fergus · 2018
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Vision-based high speed driving with a deep dynamic observer
Paul Drews, Grady Williams, Brian Goldfain, Evangelos A. Theodorou, and James M. Rehg · 2018
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Visual foresight: Model-based deep reinforcement learning for vision-based robotic control
Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, and Sergey Levine · 2018
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David Ha and Jürgen Schmidhuber · 2018
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Brian Paden, Michal Cap, Sze Zheng Yong, Dmitry Yershov, and Emilio Frazzoli · 2016
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Scenario model predictive control for lane change assistance and autonomous driving on highways
Gianluca Cesari, Georg Schildbach, Ashwin Carvalho, and Francesco Borrelli · 2017
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Stochastic model predictive control — how does it work?
Tor Aksel N. Heirung, Joel A. Paulson, Jared O’Leary, and Ali Mesbah · 2017
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Prediction under uncertainty with error-encoding networks
Mikael Henaff, Junbo Zhao, and Yann LeCun · 2017
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
Anusha Nagabandi, Gregory Kahn, Ronald S. Fearing, and Sergey Levine · 2017
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Stochastic variational video prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy H. Campbell, and Sergey Levine · 2018
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Xiaojing Zhang, Alexander Liniger, and Francesco Borrelli · 2018
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Mastering atari with discrete world models
Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba · 2020
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Simnet: Learning reactive self-driving simulations from real-world observations
Luca Bergamini, Yawei Ye, Oliver Scheel, Long Chen, Chih Hu, Luca Del Pero, Blazej Osinski, Hugo Grimmett, and Peter Ondruska · 2021
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Alexey Kamenev, Lirui Wang, Ollin Boer Bohan, Ishwar Kulkarni, Bilal Kartal, Artem Molchanov, Stan Birchfield, David Nistér, and Nikolai Smolyanskiy · 2021
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Urban driver: Learning to drive from real-world demonstrations using policy gradients
Oliver Scheel, Luca Bergamini, Maciej Wolczyk, Blazej Osinski, and Peter Ondruska · 2021
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Trafficsim: Learning to simulate realistic multi-agent behaviors
Simon Suo, Sebastian Regalado, Sergio Casas, and Raquel Urtasun · 2021
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