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Existing evaluation paradigms for Autonomous Vehicles (AVs) face critical limitations.
Robustness results in linear-quadratic gaussian based multivariable control designs
N. Lehtomaki, N. Sandell, and M. Athans · 1981
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Congested traffic states in empirical observations and microscopic simulations
M. Treiber, A. Hennecke, and D. Helbing · 2000
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The DARPA urban challenge: autonomous vehicles in city traffic
M. Buehler, K. Iagnemma, and S. Singh · 2009
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Vehicle dynamics and control
R. Rajamani · 2011
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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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Structure-from-motion revisited
J. L. Schönberger and J.-M. Frahm · 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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On offline evaluation of vision-based driving models
F. Codevilla, A. M. Lopez, V. Koltun, and A. Dosovitskiy · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 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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Counterfactual data augmentation using locally factored dynamics
S. Pitis, E. Creager, and A. Garg · 2020
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Learning by cheating
D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl · 2020
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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
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Learning robust control policies for end-to-end autonomous driving from data-driven simulation
A. Amini, I. Gilitschenski, J. Phillips, J. Moseyko, R. Banerjee, S. Karaman, and D. Rus · 2020
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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
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Fighting copycat agents in behavioral cloning from observation histories
C. Wen, J. Lin, T. Darrell, D. Jayaraman, and Y. Gao · 2020
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Label efficient visual abstractions for autonomous driving
A. Behl, K. Chitta, A. Prakash, E. Ohn-Bar, and A. Geiger · 2020
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Reinforced counterfactual data augmentation for dual sentiment classification
H. Chen, R. Xia, and J. Yu · 2021
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Neural scene graphs for dynamic scenes
J. Ost, F. Mannan, N. Thuerey, J. Knodt, and F. Heide · 2021
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Urban driver: Learning to drive from real-world demonstrations using policy gradients
O. Scheel, L. Bergamini, M. Wolczyk, B. Osiński, and P. Ondruska · 2021
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Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning
Q. Li, Z. Peng, L. Feng, Q. Zhang, Z. Xue, and B. Zhou · 2022
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Mocoda: Model-based counterfactual data augmentation
S. Pitis, E. Creager, A. Mandlekar, and A. Garg · 2022
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Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world
E. Vinitsky, N. Lichtlé, X. Yang, B. Amos, and J. Foerster · 2022
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Vista 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles
A. Amini, T.-H. Wang, I. Gilitschenski, W. Schwarting, Z. Liu, S. Han, S. Karaman, and D. Rus · 2022
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Learning interactive driving policies via data-driven simulation
T.-H. Wang, A. Amini, W. Schwarting, I. Gilitschenski, S. Karaman, and D. Rus · 2022
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Plant: Explainable planning transformers via object-level representations
K. Renz, K. Chitta, O.-B. Mercea, S. Koepke, Z. Akata, and A. Geiger · 2022
Cited alongside, same era.
BEVFormer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers
Z. Li, W. Wang, H. Li, E. Xie, C. Sima, T. Lu, Y. Qiao, and J. Dai · 2022
Cited alongside, same era.
King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients
N. Hanselmann, K. Renz, K. Chitta, A. Bhattacharyya, and A. Geiger · 2022
Cited alongside, same era.
End-to-end autonomous driving: Challenges and frontiers
L. Chen, P. Wu, K. Chitta, B. Jaeger, A. Geiger, and H. Li · 2023
Cited alongside, same era.
3d gaussian splatting for real-time radiance field rendering
B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis · 2023
Cited alongside, same era.
Bench2drive-r: Turning real world data into reactive closed-loop autonomous driving benchmark by generative model
J. You, X. Jia, Z. Zhang, Y. Zhu, and J. Yan · 2024
Later among the works it cites.
Drivelm: Driving with graph visual question answering
C. Sima, K. Renz, K. Chitta, L. Chen, H. Zhang, C. Xie, J. Beißwenger, P. Luo, A. Geiger, and H. Li · 2024
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GS-SLAM: Dense visual slam with 3d gaussian splatting
C. Yan, D. Qu, D. Xu, B. Zhao, Z. Wang, D. Wang, and X. Li · 2024
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Hydra-mdp: End-to-end multimodal planning with multi-target hydra-distillation
Z. Li, K. Li, S. Wang, S. Lan, Z. Yu, Y. Ji, Z. Li, Z. Zhu, J. Kautz, Z. Wu, Y.-G. Jiang, and J. M. Alvarez · 2024
Later among the works it cites.
Street Gaussians: Modeling dynamic urban scenes with gaussian splatting
Y. Yan, H. Lin, C. Zhou, W. Wang, H. Sun, K. Zhan, X. Lang, X. Zhou, and S. Peng · 2024
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K. Chitta, A. Prakash, B. Jaeger, Z. Yu, K. Renz, and A. Geiger · 2023
Cited alongside, same era.
Parting with misconceptions about learning-based vehicle motion planning
D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta · 2023
Cited alongside, same era.
Hidden biases of end-to-end driving models
B. Jaeger, K. Chitta, and A. Geiger · 2023
Cited alongside, same era.
Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research
C. Gulino, J. Fu, W. Luo, G. Tucker, E. Bronstein, Y. Lu, J. Harb, X. Pan, Y. Wang, X. Chen, J. D. Co-Reyes, R. Agarwal, R. Roelofs, Y. Lu, N. Montali, P. Mougin, Z. Yang, B. White, A. Faust, R. McAllister, D. Anguelov, and B. Sapp · 2023
Cited alongside, same era.
Unisim: A neural closed-loop sensor simulator
Z. Yang, Y. Chen, J. Wang, S. Manivasagam, W.-C. Ma, A. J. Yang, and R. Urtasun · 2023
Cited alongside, same era.
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
Cited alongside, same era.
KISS-ICP: In Defense of Point-to-Point ICP – Simple, Accurate, and Robust Registration If Done the Right Way
I. Vizzo, T. Guadagnino, B. Mersch, L. Wiesmann, J. Behley, and C. Stachniss · 2023
Cited alongside, same era.
Are nerfs ready for autonomous driving? towards closing the real-to-simulation gap
C. Lindström, G. Hess, A. Lilja, M. Fatemi, L. Hammarstrand, C. Petersson, and L. Svensson · 2024
Later among the works it cites.
Generalized Predictive Model for Autonomous Driving
J. Yang, S. Gao, Y. Qiu, L. Chen, T. Li, B. Dai, K. Chitta, P. Wu, J. Zeng, P. Luo, J. Zhang, A. Geiger, Y. Qiao, and H. Li · 2024
Later among the works it cites.
Vista: A generalizable driving world model with high fidelity and versatile controllability
S. Gao, J. Yang, L. Chen, K. Chitta, Y. Qiu, A. Geiger, J. Zhang, and H. Li · 2024
Later among the works it cites.
Sledge: Synthesizing driving environments with generative models and rule-based traffic
K. Chitta, D. Dauner, and A. Geiger · 2024
Later among the works it cites.
MTGS: Multi-traversal gaussian splatting
T. Li, Y. Qiu, Z. Wu, C. Lindström, P. Su, M. Nießner, and H. Li · 2025
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Simlingo: Vision-only closed-loop autonomous driving with language-action alignment
K. Renz, L. Chen, E. Arani, and O. Sinavski · 2025
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Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation
K. Li, Z. Li, S. Lan, Y. Xie, Z. Zhang, J. Liu, Z. Wu, Z. Yu, and J. M. Alvarez · 2025
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Rap: 3d rasterization augmented end-to-end planning
L. Feng, Y. Gao, E. Zablocki, Q. Li, W. Li, S. Liu, M. Cord, and A. Alahi · 2025
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Ztrs: Zero-imitation end-to-end autonomous driving with trajectory scoring
Z. Li, W. Yao, Z. Wang, X. Sun, J. Chen, N. Chang, M. Shen, J. Song, Z. Wu, S. Lan, and J. M. Alvarez · 2025
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Guideflow: Constraint-guided flow matching for planning in end-to-end autonomous driving
L. Liu, C. Jia, G. Yu, Z. Song, J. Li, F. Jia, P. Wu, X. Hao, and Y. Luo · 2025
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Leveraging correlation across test platforms for variance-reduced metric estimation
R. Luo, H. Yang, M. Watson, A. Sharma, S. Veer, E. Schmerling, and M. Pavone · 2025
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Storm: Spatio-temporal reconstruction model for large-scale outdoor scenes
J. Yang, J. Huang, Y. Chen, Y. Wang, B. Li, Y. You, M. Igl, A. Sharma, P. Karkus, D. Xu, B. Ivanovic, Y. Wang, and M. Pavone · 2025
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Evolsplat: Efficient volume-based gaussian splatting for urban view synthesis
S. Miao, J. Huang, D. Bai, X. Yan, H. Zhou, Y. Wang, B. Liu, A. Geiger, and Y. Liao · 2025
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Resim: Reliable world simulation for autonomous drivingend to end learning for self-driving cars
J. Yang, K. Chitta, S. Gao, L. Chen, Y. Shao, X. Jia, H. Li, A. Geiger, X. Yue, and L. Chen · 2025
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Difix3d+: Improving 3d reconstructions with single-step diffusion models
J. Z. Wu, Y. Zhang, H. Turki, X. Ren, J. Gao, M. Z. Shou, S. Fidler, Z. Gojcic, and H. Ling · 2025
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Closed-loop supervised fine-tuning of tokenized traffic models
Z. Zhang, P. Karkus, M. Igl, W. Ding, Y. Chen, B. Ivanovic, and M. Pavone · 2025
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Lead: Minimizing learner-expert asymmetry in end-to-end driving
L. Nguyen, M. Fauth, B. Jaeger, D. Dauner, M. Igl, A. Geiger, and K. Chitta · 2026
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Simscale: Learning to drive via real-world simulation at scale
H. Tian, T. Li, H. Liu, J. Yang, Y. Qiu, G. Li, J. Wang, Y. Gao, Z. Zhang, L. Wang, H. Ye, T. Tan, L. Chen, and H. Li · 2026
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Driving on registers
E. Kirby, A. Boulch, Y. Xu, Y. Yin, G. Puy, É. Zablocki, A. Bursuc, S. Gidaris, R. Marlet, F. Bartoccioni, A.-Q. Cao, N. Samet, T.-H. VU, and M. Cord · 2026
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