Fetching the paper…
Reading the bibliography…
Traditional planning and control methods could fail to find a feasible trajectory for an autonomous vehicle to execute amongst dense traffic on roads.
Alvinn: An autonomous land vehicle in a neural network
Dean Pomerleau · 1989
Earlier work this paper cites.
Congested traffic states in empirical observations and microscopic simulations
Martin Treiber, Ansgar Hennecke, and Dirk Helbing · 2000
Earlier work this paper cites.
Optimal rough terrain trajectory generation for wheeled mobile robots
Thomas M. Howard and Alonzo Kelly · 2007
Earlier work this paper cites.
The DARPA Urban Challenge: Autonomous Vehicles in City Traffic, George Air Force Base, Victorville, California, USA
Martin Buehler, Karl Iagnemma, and Sanjiv Singh, editors · 2009
Earlier work this paper cites.
Modeling lane-changing decisions with mobil
Martin Treiber and Arne Kesting · 2009
Earlier work this paper cites.
Unfreezing the robot: Navigation in dense, interacting crowds
P. Trautman and A. Krause · 2010
Earlier work this paper cites.
Towards fully autonomous driving: Systems and algorithms
Jesse Levinson, Jake Askeland, Jan Becker, Jennifer Dolson, David Held, Soeren Kammel, J Zico Kolter, Dirk Langer, Oliver Pink, Vaughan Pratt, et al · 2011
Earlier work this paper cites.
A real-time motion planner with trajectory optimization for autonomous vehicles
Wenda Xu, Junqing Wei, John M. Dolan, Huijing Zhao, and Hongbin Zha · 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.
Real-time motion planning methods for autonomous on-road driving: State-of-the-art and future research directions
Christos Katrakazas, Mohammed Quddus, Wen-Hua Chen, and Lipika Deka · 2015
Earlier work this paper cites.
Kinematic and dynamic vehicle models for autonomous driving control design
J. Kong, M. Pfeiffer, G. Schildbach, and F. Borrelli · 2015
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Earlier work this paper cites.
Efficient sampling-based motion planning for on-road autonomous driving
Liang Ma, Jianru Xue, Kuniaki Kawabata, Jihua Zhu, Chao Ma, and Nanning Zheng · 2015
Earlier work this paper cites.
Structuring cooperative behavior planning implementations for automated driving
S. Ulbrich, S. Grossjohann, C. Appelt, K. Homeier, J. Rieken, and M. Maurer · 2015
Cited alongside, same era.
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, Xin Zhang, Jake Zhao, and Karol Zieba · 2016
Cited alongside, same era.
A survey of motion planning and control techniques for self-driving urban vehicles
Brian Paden, Michal Cáp, Sze Zheng Yong, Dmitry S. Yershov, and Emilio Frazzoli · 2016
Cited alongside, same era.
Planning for autonomous cars that leverage effects on human actions
Dorsa Sadigh, Shankar Sastry, Sanjit A. Seshia, and Anca D. Dragan · 2016
Cited alongside, same era.
Safe, multi-agent, reinforcement learning for autonomous driving
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2016
Variational autoencoder for end-to-end control of autonomous driving with novelty detection and training de-biasing
Alexander Amini, Wilko Schwarting, Guy Rosman, Brandon Araki, Sertac Karaman, and Daniela Rus · 2018
Later among the works it cites.
End-to-end driving via conditional imitation learning
Felipe Codevilla, Matthias Miiller, Antonio López, Vladlen Koltun, and Alexey Dosovitskiy · 2018
Later among the works it cites.
An introduction to deep reinforcement learning
Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G Bellemare, Joelle Pineau, et al · 2018
Later among the works it cites.
Social GAN: socially acceptable trajectories with generative adversarial networks
Agrim Gupta, Justin Johnson, Li Fei-Fei, Silvio Savarese, and Alexandre Alahi · 2018
Later among the works it cites.
Planning and decision-making for autonomous vehicles
Wilko Schwarting, Javier Alonso-Mora, and Daniela Rus · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Improving stochastic policy gradients in continuous control with deep reinforcement learning using the beta distribution
Po-Wei Chou, Daniel Maturana, and Sebastian Scherer · 2017
Cited alongside, same era.
Control of a quadrotor with reinforcement learning
J. Hwangbo, I. Sa, R. Siegwart, and M. Hutter · 2017
Cited alongside, same era.
Imitating driver behavior with generative adversarial networks
Alex Kuefler, Jeremy Morton, Tim Allan Wheeler, and Mykel J. Kochenderfer · 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.
Parallel autonomy in automated vehicles: Safe motion generation with minimal intervention
W. Schwarting, J. Alonso-Mora, L. Pauli, S. Karaman, and D. Rus · 2017
Cited alongside, same era.
Learning how to drive in a real world simulation with deep q-networks
Peter Wolf, Christian Hubschneider, Michael Weber, Andre Bauer, Jonathan Hartl, Fabian Durr, and Johann Marius Zöllner · 2017
Cited alongside, same era.
Search-based optimal motion planning for automated driving
Z. Ajanovic, B. Lacevic, B. Shyrokau, M. Stolz, and M. Horn · 2018
Cited alongside, same era.
Sangjae Bae, Dhruv Saxena, Alireza Nakhaei, Chiho Choi, Kikuo Fujimura, and Scott Moura · 2019
Closest in time.
Model-free deep reinforcement learning for urban autonomous driving
Jianyu Chen, Bodi Yuan, and Masayoshi Tomizuka · 2019
Closest in time.
Hierarchical game-theoretic planning for autonomous vehicles
Jaime F. Fisac, Eli Bronstein, Elis Stefansson, Dorsa Sadigh, S. Shankar Sastry, and Anca D. Dragan · 2019
Closest in time.
Model-predictive policy learning with uncertainty regularization for driving in dense traffic
Mikael Henaff, Alfredo Canziani, and Yann LeCun · 2019
Closest in time.
Carl-Johan Hoel, Katherine Rose Driggs-Campbell, Krister Wolff, Leo Laine, and Mykel J. Kochenderfer · 2019
Closest in time.
Interaction-aware decision making with adaptive strategies under merging scenarios
Yeping Hu, Alireza Nakhaei, Masayoshi Tomizuka, and Kikuo Fujimura · 2019
Closest in time.
Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, and Marco Hutter · 2019
Closest in time.