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Realistic traffic simulation is crucial for developing self-driving software in a safe and scalable manner prior to real-world deployment.
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Planning algorithms
S. M. LaValle · 2006
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Mobil : General lane-changing model for car-following models
A. Kesting · 2007
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Pre-crash scenario typology for crash avoidance research
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Matsim-t: Architecture and simulation times
M. Balmer, M. Rieser, K. Meister, D. Charypar, N. Lefebvre, and K. Nagel · 2009
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Traffic simulation with aimsun
J. Casas, J. L. Ferrer, D. Garcia, J. Perarnau, and A. Torday · 2010
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Traffic simulation with mitsimlab
M. Ben-Akiva, H. N. Koutsopoulos, T. Toledo, Q. Yang, C. F. Choudhury, C. Antoniou, and R. Balakrishna · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. Gordon, and D. Bagnell · 2011
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
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High-dimensional continuous control using generalized advantage estimation
J. Schulman, P. Moritz, S. Levine, M. Jordan, and P. Abbeel · 2015
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Safe, multi-agent, reinforcement learning for autonomous driving
S. Shalev-Shwartz, S. Shammah, and A. Shashua · 2016
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End to end learning for self-driving cars
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al · 2016
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Generative adversarial imitation learning
J. Ho and S. Ermon · 2016
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Virtual to real reinforcement learning for autonomous driving
X. Pan, Y. You, Z. Wang, and C. Lu · 2017
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Learning robust rewards with adversarial inverse reinforcement learning
J. Fu, K. Luo, and S. Levine · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
M. Vecerik, T. Hester, J. Scholz, F. Wang, O. Pietquin, B. Piot, N. Heess, T. Rothörl, T. Lampe, and M. Riedmiller · 2017
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
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Microscopic traffic simulation using sumo
P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner · 2018
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End-to-end driving via conditional imitation learning
F. Codevilla, M. Müller, A. López, V. Koltun, and A. Dosovitskiy · 2018
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Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
M. Bansal, A. Krizhevsky, and A. Ogale · 2018
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Soft actor-critic algorithms and applications
T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel, et al · 2018
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Reinforcement and imitation learning for diverse visuomotor skills
Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas, et al · 2018
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Deep q-learning from demonstrations
T. Hester, M. Vecerik, O. Pietquin, M. Lanctot, T. Schaul, B. Piot, D. Horgan, J. Quan, A. Sendonaris, I. Osband, et al · 2018
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Cirl: Controllable imitative reinforcement learning for vision-based self-driving
X. Liang, T. Wang, L. Yang, and E. Xing · 2018
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Scenarios for development, test and validation of automated vehicles
T. Menzel, G. Bagschik, and M. Maurer · 2018
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Trafficsim: Learning to simulate realistic multi-agent behaviors
S. Suo, S. Regalado, S. Casas, and R. Urtasun · 2021
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Imagining the road ahead: Multi-agent trajectory prediction via differentiable simulation
A. Ścibior, V. Lioutas, D. Reda, P. Bateni, and F. Wood · 2021
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A minimalist approach to offline reinforcement learning
S. Fujimoto and S. S. Gu · 2021
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Advsim: Generating safety-critical scenarios for self-driving vehicles
J. Wang, A. Pun, J. Tu, S. Manivasagam, A. Sadat, S. Casas, M. Ren, and R. Urtasun · 2021
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Y. Lu, J. Fu, G. Tucker, X. Pan, E. Bronstein, B. Roelofs, B. Sapp, B. White, A. Faust, S. Whiteson, et al · 2022
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Scenarios for development, test and validation of automated vehicles
T. Menzel, G. Bagschik, and M. Maurer · 2018
Cited alongside, same era.
Model-predictive policy learning with uncertainty regularization for driving in dense traffic
M. Henaff, A. Canziani, and Y. LeCun · 2019
Cited alongside, same era.
A framework for definition of logical scenarios for safety assurance of automated driving
H. Weber, J. Bock, J. Klimke, C. Roesener, J. Hiller, R. Krajewski, A. Zlocki, and L. Eckstein · 2019
Cited alongside, same era.
End-to-end model-free reinforcement learning for urban driving using implicit affordances
M. Toromanoff, E. Wirbel, and F. Moutarde · 2020
Cited alongside, same era.
Disagreement-regularized imitation learning
K. Brantley, W. Sun, and M. Henaff · 2020
Cited alongside, same era.
A divergence minimization perspective on imitation learning methods
S. K. S. Ghasemipour, R. Zemel, and S. Gu · 2020
Cited alongside, same era.
Awac: Accelerating online reinforcement learning with offline datasets
A. Nair, A. Gupta, M. Dalal, and S. Levine · 2020
Cited alongside, same era.
E. Vinitsky, N. Lichtlé, X. Yang, B. Amos, and J. Foerster · 2022
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Rethinking closed-loop training for autonomous driving
C. Zhang, R. Guo, W. Zeng, Y. Xiong, B. Dai, R. Hu, M. Ren, and R. Urtasun · 2022
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Symphony: Learning realistic and diverse agents for autonomous driving simulation, 2022
M. Igl, D. Kim, A. Kuefler, P. Mougin, P. Shah, K. Shiarlis, D. Anguelov, M. Palatucci, B. White, and S. Whiteson · 2022
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Titrated: Learned human driving behavior without infractions via amortized inference
V. Lioutas, A. Scibior, and F. Wood · 2022
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Jump-start reinforcement learning
I. Uchendu, T. Xiao, Y. Lu, B. Zhu, M. Yan, J. Simon, M. Bennice, C. Fu, C. Ma, J. Jiao, et al · 2022
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Aw-opt: Learning robotic skills with imitation and reinforcement at scale
Y. Lu, K. Hausman, Y. Chebotar, M. Yan, E. Jang, A. Herzog, T. Xiao, A. Irpan, M. Khansari, D. Kalashnikov, et al · 2022
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Training language models to follow instructions with human feedback
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. L. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
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Predictionnet: Real-time joint probabilistic traffic prediction for planning, control, and simulation
A. Kamenev, L. Wang, O. B. Bohan, I. Kulkarni, B. Kartal, A. Molchanov, S. Birchfield, D. Nistér, and N. Smolyanskiy · 2022
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Trajgen: Generating realistic and diverse trajectories with reactive and feasible agent behaviors for autonomous driving
Q. Zhang, Y. Gao, Y. Zhang, Y. Guo, D. Ding, Y. Wang, P. Sun, and D. Zhao · 2022
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Embedding synthetic off-policy experience for autonomous driving via zero-shot curricula
E. Bronstein, S. Srinivasan, S. Paul, A. Sinha, M. O’Kelly, P. Nikdel, and S. Whiteson · 2022
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Generating useful accident-prone driving scenarios via a learned traffic prior
D. Rempe, J. Philion, L. J. Guibas, S. Fidler, and O. Litany · 2022
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King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients
N. Hanselmann, K. Renz, K. Chitta, A. Bhattacharyya, and A. Geiger · 2022
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Gorela: Go relative for viewpoint-invariant motion forecasting
A. Cui, S. Casas, K. Wong, S. Suo, and R. Urtasun · 2022
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Trafficgen: Learning to generate diverse and realistic traffic scenarios
L. Feng, Q. Li, Z. Peng, S. Tan, and B. Zhou · 2023
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Bits: Bi-level imitation for traffic simulation
D. Xu, Y. Chen, B. Ivanovic, and M. Pavone · 2023
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Mixsim: A hierarchical framework for mixed reality traffic simulation
S. Suo, K. Wong, J. Xu, J. Tu, A. Cui, S. Casas, and R. Urtasun · 2023
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Guided conditional diffusion for controllable traffic simulation
Z. Zhong, D. Rempe, D. Xu, Y. Chen, S. Veer, T. Che, B. Ray, and M. Pavone · 2023
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