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The release of nuPlan marks a new era in vehicle motion planning research, offering the first large-scale real-world dataset and evaluation schemes requiring both precise short-term planning and long-horizon ego-forecasting.
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M. Treiber, A. Hennecke, and D. Helbing · 2000
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Stanley: The robot that won the DARPA grand challenge
S. Thrun, M. Montemerlo, H. Dahlkamp, D. Stavens, A. Aron, J. Diebel, P. Fong, J. Gale, M. Halpenny, G. Hoffmann, K. Lau, C. M. Oakley, M. Palatucci, V. R. Pratt, P. Stang, S. Strohband, C. Dupont, L. Jendrossek, C. Koelen, C. Markey, C. Rummel, J. van Niekerk, E. Jensen, P. Alessandrini, G. R. Bradski, B. Davies, S. Ettinger, A. Kaehler, A. V. Nefian, and P. Mahoney · 2006
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Odin: Team victortango’s entry in the darpa urban challenge
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A perception-driven autonomous urban vehicle
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Autonomous driving in urban environments: Boss and the urban challenge
C. Urmson, J. Anhalt, D. Bagnell, C. Baker, R. Bittner, M. Clark, J. Dolan, D. Duggins, T. Galatali, C. Geyer, et al · 2008
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A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. J. Gordon, and D. Bagnell · 2011
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Vehicle dynamics and control
R. Rajamani · 2011
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Control-limited differential dynamic programming
Y. Tassa, N. Mansard, and E. Todorov · 2014
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Deepdriving: Learning affordance for direct perception in autonomous driving
C. Chen, A. Seff, A. L. Kornhauser, and J. Xiao · 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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End-to-end learning of driving models from large-scale video datasets
H. Xu, Y. Gao, F. Yu, and T. Darrell · 2017
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Commonroad: Composable benchmarks for motion planning on roads
M. Althoff, M. Koschi, and S. Manzinger · 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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End-to-end driving via conditional imitation learning
F. Codevilla, M. Miiller, A. López, V. Koltun, and A. Dosovitskiy · 2018
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A. Kendall, J. Hawke, D. Janz, P. Mazur, D. Reda, J. M. Allen, V. D. Lam, A. Bewley, and A. Shah · 2018
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On offline evaluation of vision-based driving models
F. Codevilla, A. M. Lopez, 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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Baidu apollo EM motion planner
H. Fan, F. Zhu, C. Liu, L. Zhang, L. Zhuang, D. Li, W. Zhu, J. Hu, H. Li, and Q. Kong · 2018
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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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PRECOG: prediction conditioned on goals in visual multi-agent settings
N. Rhinehart, R. McAllister, K. Kitani, and S. Levine · 2019
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End-to-end interpretable neural motion planner
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun · 2019
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Chauffeurnet: Learning to drive by imitating the best and synthesizing the worst
M. Bansal, A. Krizhevsky, and A. S. Ogale · 2019
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Jointly learnable behavior and trajectory planning for self-driving vehicles
A. Sadat, M. Ren, A. Pokrovsky, Y. Lin, E. Yumer, and R. Urtasun · 2019
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Learning by cheating
D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl · 2019
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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
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Pip: Planning-informed trajectory prediction for autonomous driving
H. Song, W. Ding, Y. Chen, S. Shen, M. Y. Wang, and Q. Chen · 2020
Cited alongside, same era.
Deep imitative models for flexible inference, planning, and control
N. Rhinehart, R. McAllister, and S. Levine · 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.
Learning situational driving
E. Ohn-Bar, A. Prakash, A. Behl, K. Chitta, and A. Geiger · 2020
Cited alongside, same era.
Label efficient visual abstractions for autonomous driving
A. Behl, K. Chitta, A. Prakash, E. Ohn-Bar, and A. Geiger · 2020
Cited alongside, same era.
nuscenes: A multimodal dataset for autonomous driving
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom · 2020
St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning
S. Hu, L. Chen, P. Wu, H. Li, J. Yan, and D. Tao · 2022
Later among the works it cites.
Trajectory-guided control prediction for end-to-end autonomous driving: A simple yet strong baseline
P. Wu, X. Jia, L. Chen, J. Yan, H. Li, and Y. Qiao · 2022
Later among the works it cites.
Learning from all vehicles
D. Chen and P. Krähenbühl · 2022
Later among the works it cites.
King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients
N. Hanselmann, K. Renz, K. Chitta, A. Bhattacharyya, and A. Geiger · 2022
Later among the works it cites.
Safetynet: Safe planning for real-world self-driving vehicles using machine-learned policies
M. Vitelli, Y. Chang, Y. Ye, A. Ferreira, M. Wołczyk, B. Osiński, M. Niendorf, H. Grimmett, Q. Huang, A. Jain, et al · 2022
Later among the works it cites.
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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.
What the constant velocity model can teach us about pedestrian motion prediction
C. Schöller, V. Aravantinos, F. Lay, and A. Knoll · 2020
Cited alongside, same era.
nuplan: A closed-loop ml-based planning benchmark for autonomous vehicles
H. Caesar, J. Kabzan, K. S. Tan, W. K. Fong, E. M. Wolff, A. H. Lang, L. Fletcher, O. Beijbom, and S. Omari · 2021
Cited alongside, same era.
Urban driver: Learning to drive from real-world demonstrations using policy gradients
O. Scheel, L. Bergamini, M. Wolczyk, B. Osiński, and P. Ondruska · 2021
Cited alongside, same era.
Multi-modal fusion transformer for end-to-end autonomous driving
A. Prakash, K. Chitta, and A. Geiger · 2021
Cited alongside, same era.
Neat: Neural attention fields for end-to-end autonomous driving
K. Chitta, A. Prakash, and A. Geiger · 2021
Cited alongside, same era.
M. Hallgarten, M. Stoll, and A. Zell · 2023
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Planning-oriented autonomous driving
Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang, L. Lu, X. Jia, Q. Liu, J. Dai, Y. Qiao, and H. Li · 2023
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Mbappe: Mcts-built-around prediction for planning explicitly
R. Chekroun, T. Gilles, M. Toromanoff, S. Hornauer, and F. Moutarde · 2023
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Dtpp: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving
Z. Huang, P. Karkus, B. Ivanovic, Y. Chen, M. Pavone, and C. Lv · 2023
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Driveirl: Drive in real life with inverse reinforcement learning
T. Phan-Minh, F. Howington, T.-S. Chu, M. S. Tomov, R. E. Beaudoin, S. U. Lee, N. Li, C. Dicle, S. Findler, F. Suarez-Ruiz, et al · 2023
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Tree-structured policy planning with learned behavior models
Y. Chen, P. Karkus, B. Ivanovic, X. Weng, and M. Pavone · 2023
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Gameformer: Game-theoretic modeling and learning of transformer-based interactive prediction and planning for autonomous driving
Z. Huang, H. Liu, and C. Lv · 2023
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End-to-end autonomous driving: Challenges and frontiers
L. Chen, P. Wu, K. Chitta, B. Jaeger, A. Geiger, and H. Li · 2023
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Hidden biases of end-to-end driving models
B. Jaeger, K. Chitta, and A. Geiger · 2023
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Safe real-world autonomous driving by learning to predict and plan with a mixture of experts
S. Pini, C. S. Perone, A. Ahuja, A. S. R. Ferreira, M. Niendorf, and S. Zagoruyko · 2023
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Rethinking imitation-based planner for autonomous driving
J. Cheng, Y. Chen, X. Mei, B. Yang, B. Li, and M. Liu · 2023
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Rethinking the open-loop evaluation of end-to-end autonomous driving in nuscenes
J.-T. Zhai, Z. Feng, J. Du, Y. Mao, J.-J. Liu, Z. Tan, Y. Zhang, X. Ye, and J. Wang · 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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Bits: Bi-level imitation for traffic simulation
D. Xu, Y. Chen, B. Ivanovic, and M. Pavone · 2023
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TrafficBots: Towards world models for autonomous driving simulation and motion prediction
Z. Zhang, A. Liniger, D. Dai, F. Yu, and L. Van Gool · 2023
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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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From crowd motion prediction to robot navigation in crowds
S. Poddar, C. Mavrogiannis, and S. S. Srinivasa · 2023
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What truly matters in trajectory prediction for autonomous driving?
H. Wu, T. Phong, C. Yu, P. Cai, S. Zheng, and D. Hsu · 2023
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Wayformer: Motion forecasting via simple and efficient attention networks
N. Nayakanti, R. Al-Rfou, A. Zhou, K. Goel, K. S. Refaat, and B. Sapp · 2023
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Gorela: Go relative for viewpoint-invariant motion forecasting
A. Cui, S. Casas, K. Wong, S. Suo, and R. Urtasun · 2023
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Imitation with spatial-temporal heatmap: 2nd place solution for nuplan challenge
Y. Hu, K. Li, P. Liang, J. Qian, Z. Yang, H. Zhang, W. Shao, Z. Ding, W. Xu, and Q. Liu · 2023
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