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Simulators offer the possibility of safe, low-cost development of self-driving systems.
ALVINN: an autonomous land vehicle in a neural network
D. Pomerleau · 1988
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Completely derandomized self-adaptation in evolution strategies
N. Hansen and A. Ostermeier · 2001
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
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End to end learning for self-driving cars
M. Bojarski, D. D. Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba · 2016
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CARLA: An open urban driving simulator
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun · 2017
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Playing for benchmarks
S. R. Richter, Z. Hayder, and V. Koltun · 2017
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The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?
P. Polack, F. Altché, B. d’Andréa Novel, and A. de La Fortelle · 2017
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Scalable end-to-end autonomous vehicle testing via rare-event simulation
M. O' Kelly, A. Sinha, H. Namkoong, R. Tedrake, and J. C. Duchi · 2018
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End-to-end driving via conditional imitation learning
F. Codevilla, M. Miiller, A. López, V. Koltun, and A. Dosovitskiy · 2018
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Conditional affordance learning for driving in urban environments
A. Sauer, N. Savinov, and A. Geiger · 2018
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Efficient black-box assessment of autonomous vehicle safety
J. Norden, M. O’Kelly, and A. Sinha · 2019
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Meta-sim: Learning to generate synthetic datasets
A. Kar, A. Prakash, M. Liu, E. Cameracci, J. Yuan, M. Rusiniak, D. Acuna, A. Torralba, and S. Fidler · 2019
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Generating adversarial driving scenarios in high-fidelity simulators
Y. Abeysirigoonawardena, F. Shkurti, and G. Dudek · 2019
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Simple black-box adversarial attacks
C. Guo, J. R. Gardner, Y. You, A. G. Wilson, and K. Q. Weinberger · 2019
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Prior convictions: Black-box adversarial attacks with bandits and priors
A. Ilyas, L. Engstrom, and A. Madry · 2019
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Exploring the limitations of behavior cloning for autonomous driving
F. Codevilla, E. Santana, A. M. López, and A. Gaidon · 2019
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End-to-end multi-view fusion for 3d object detection in lidar point clouds
Y. Zhou, P. Sun, Y. Zhang, D. Anguelov, J. Gao, T. Ouyang, J. Guo, J. Ngiam, and V. Vasudevan · 2019
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Learning by cheating
D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl · 2019
Cited alongside, same era.
End-to-end interpretable neural motion planner
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun · 2019
Cited alongside, same era.
Computer Vision for Autonomous Vehicles: Problems, Datasets and State of the Art , volume 12
J. Janai, F. Güney, A. Behl, and A. Geiger · 2020
Cited alongside, same era.
Can autonomous vehicles identify, recover from, and adapt to distribution shifts?
A. Filos, P. Tigas, R. McAllister, N. Rhinehart, S. Levine, and Y. Gal · 2020
Cited alongside, same era.
https://leaderboard.carla.org/ , 2020
Carla autonomous driving leaderboard · 2020
Cited alongside, same era.
Scenic: A language for scenario specification and data generation
D. J. Fremont, E. Kim, T. Dreossi, S. Ghosh, X. Yue, A. L. Sangiovanni-Vincentelli, and S. A. Seshia · 2020
Fishing net: Future inference of semantic heatmaps in grids
N. Hendy, C. Sloan, F. Tian, P. Duan, N. Charchut, Y. Xie, C. Wang, and J. Philbin · 2020
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Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d
J. Philion and S. Fidler · 2020
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Driving among Flatmobiles: Bird-Eye-View occupancy grids from a monocular camera for holistic trajectory planning
A. Loukkal, Y. Grandvalet, T. Drummond, and Y. Li · 2020
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Learning to collide: An adaptive safety-critical scenarios generating method
W. Ding, M. Xu, and D. Zhao · 2020
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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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Cited alongside, same era.
Square attack: A query-efficient black-box adversarial attack via random search
M. Andriushchenko, F. Croce, N. Flammarion, and M. Hein · 2020
Cited alongside, same era.
An analysis of adversarial attacks and defenses on autonomous driving models
Y. Deng, X. Zheng, T. Zhang, C. Chen, G. Lou, and M. Kim · 2020
Cited alongside, same era.
Perceive, predict, and plan: Safe motion planning through interpretable semantic representations
A. Sadat, S. Casas, M. Ren, X. Wu, P. Dhawan, and R. Urtasun · 2020
Cited alongside, same era.
Exploring data aggregation in policy learning for vision-based urban autonomous driving
A. Prakash, A. Behl, E. Ohn-Bar, K. Chitta, and A. Geiger · 2020
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.
Learning situational driving
E. Ohn-Bar, A. Prakash, A. Behl, K. Chitta, and A. Geiger · 2020
Cited alongside, same era.
A. Ścibior, V. Lioutas, D. Reda, P. Bateni, and F. Wood · 2021
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Urban driver: Learning to drive from real-world demonstrations using policy gradients
O. Scheel, L. Bergamini, M. Wolczyk, B. Osinski, and P. Ondruska · 2021
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Simnet: Learning reactive self-driving simulations from real-world observations
L. Bergamini, Y. Ye, O. Scheel, L. Chen, C. Hu, L. D. Pero, B. Osinski, H. Grimmett, and P. Ondruska · 2021
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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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Mp3: A unified model to map, perceive, predict and plan
S. Casas, A. Sadat, and R. Urtasun · 2021
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Neat: Neural attention fields for end-to-end autonomous driving
K. Chitta, A. Prakash, and A. Geiger · 2021
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Multi-modal fusion transformer for end-to-end autonomous driving
A. Prakash, K. Chitta, and A. Geiger · 2021
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Learning to drive from a world on rails
D. Chen, V. Koltun, and P. Krähenbühl · 2021
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End-to-end urban driving by imitating a reinforcement learning coach
Z. Zhang, A. Liniger, D. Dai, F. Yu, and L. Van Gool · 2021
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FIERY: future instance prediction in bird’s-eye view from surround monocular cameras
A. Hu, Z. Murez, N. Mohan, S. Dudas, J. Hawke, V. Badrinarayanan, R. Cipolla, and A. Kendall · 2021
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Multimodal safety-critical scenarios generation for decision-making algorithms evaluation
W. Ding, B. Chen, B. Li, K. J. Eun, and D. Zhao · 2021
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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 · 2021
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