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Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safety and generalization performance.
Adversarial objects against lidar-based autonomous driving systems
Y. Cao, C. Xiao, D. Yang, J. Fang, R. Yang, M. Liu, and B. Li · 1907
Earlier work this paper cites.
Mathematical theory of optimal processes
L. S. Pontryagin · 1987
Earlier work this paper cites.
Physically Realizable Adversarial Examples for LiDAR Object Detection, Apr. 2020
J. Tu, M. Ren, S. Manivasagam, M. Liang, B. Yang, R. Du, F. Cheng, and R. Urtasun · 2004
Earlier work this paper cites.
J. Yang, A. Boloor, A. Chakrabarti, X. Zhang, and Y. Vorobeychik · 2010
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. Gordon, and D. Bagnell · 2011
Earlier work this paper cites.
Bayesian optimization in high dimensions via random embeddings
Z. Wang, M. Zoghi, F. Hutter, D. Matheson, N. De Freitas, et al · 2013
Earlier work this paper cites.
Gradient estimation using stochastic computation graphs
J. Schulman, N. Heess, T. Weber, and P. Abbeel · 2015
Earlier work this paper cites.
End to end learning for self-driving cars, 2016
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
Earlier work this paper cites.
Structure-from-motion revisited
J. L. Schönberger and J.-M. Frahm · 2016
Earlier work this paper cites.
Pixelwise view selection for unstructured multi-view stereo
J. L. Schönberger, E. Zheng, M. Pollefeys, and J.-M. Frahm · 2016
Earlier work this paper cites.
Inside waymo’s secret world for training self-driving cars
A. C. Madrigal · 2017
Earlier work this paper cites.
CARLA: An open urban driving simulator
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun · 2017
Earlier work this paper cites.
End-to-end driving via conditional imitation learning
F. Codevilla, M. Müller, A. Dosovitskiy, A. M. López, and V. Koltun · 2017
Earlier work this paper cites.
Driving to Safety: How Many Miles of Driving Would It Take to Demonstrate Autonomous Vehicle Reliability?
K. Nidhi and S. M. Paddock · 2018
Earlier work this paper cites.
Adaptive stress testing for autonomous vehicles
M. Koren, S. Alsaif, R. Lee, and M. J. Kochenderfer · 2018
Earlier work this paper cites.
Synthesizing robust adversarial examples
A. Athalye, L. Engstrom, A. Ilyas, and K. Kwok · 2018
Earlier work this paper cites.
Blender - a 3D modelling and rendering package
B. O. Community · 2018
Earlier work this paper cites.
Bayesian optimization with gradients, 2018
J. Wu, M. Poloczek, A. G. Wilson, and P. I. Frazier · 2018
Earlier work this paper cites.
Generating adversarial driving scenarios in high-fidelity simulators
Y. Abeysirigoonawardena, F. Shkurti, and G. Dudek · 2019
Earlier work this paper cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Earlier work this paper cites.
End-to-end interpretable neural motion planner
W. Zeng, W. Luo, S. Suo, A. Sadat, B. Yang, S. Casas, and R. Urtasun · 2019
Earlier work this paper cites.
Differentiation of blackbox combinatorial solvers
M. Vlastelica, A. Paulus, V. Musil, G. Martius, and M. Rolínek · 2019
Earlier work this paper cites.
Differentiable convex optimization layers
A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Z. Kolter · 2019
Earlier work this paper cites.
Neural sparse voxel fields
L. Liu, J. Gu, K. Zaw Lin, T.-S. Chua, and C. Theobalt · 2020
Cited alongside, same era.
A survey of end-to-end driving: Architectures and training methods
A. Tampuu, T. Matiisen, M. Semikin, D. Fishman, and N. Muhammad · 2020
Cited alongside, same era.
Accelerating 3d deep learning with pytorch3d
N. Ravi, J. Reizenstein, D. Novotny, T. Gordon, W.-Y. Lo, J. Johnson, and G. Gkioxari · 2020
Cited alongside, same era.
Black-box optimization with local generative surrogates
S. Shirobokov, V. Belavin, M. Kagan, A. Ustyuzhanin, and A. G. Baydin · 2020
Cited alongside, same era.
Drivegan: Towards a controllable high-quality neural simulation
S. W. Kim, J. Philion, A. Torralba, and S. Fidler · 2021
Cited alongside, same era.
Geosim: Realistic video simulation via geometry-aware composition for self-driving
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
Later among the works it cites.
Targeted attack on deep rl-based autonomous driving with learned visual patterns
P. Buddareddygari, T. Zhang, Y. Yang, and Y. Ren · 2022
Later among the works it cites.
A. Byravan, J. Humplik, L. Hasenclever, A. Brussee, F. Nori, T. Haarnoja, B. Moran, S. Bohez, F. Sadeghi, B. Vujatovic, and N. Heess · 2022
Later among the works it cites.
Plenoxels: Radiance fields without neural networks
S. Fridovich-Keil, A. Yu, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa · 2022
Later among the works it cites.
Tensorf: Tensorial radiance fields
A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su · 2022
Later among the works it cites.
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Y. Chen, F. Rong, S. Duggal, S. Wang, X. Yan, S. Manivasagam, S. Xue, E. Yumer, and R. Urtasun · 2021
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Advances in adversarial attacks and defenses in computer vision: A survey, Sept. 2021
N. Akhtar, A. Mian, N. Kardan, and M. Shah · 2021
Cited alongside, same era.
Advances in neural rendering
A. Tewari, O. Fried, J. Thies, V. Sitzmann, S. Lombardi, Z. Xu, T. Simon, M. Nießner, E. Tretschk, L. Liu, B. Mildenhall, P. Srinivasan, R. Pandey, S. Orts-Escolano, S. Fanello, M. Guo, G. Wetzstein, J.-Y. Zhu, C. Theobalt, M. Agrawala, D. B. Goldman, and M. Zollhöfer · 2021
Cited alongside, same era.
gradsim: Differentiable simulation for system identification and visuomotor control
K. M. Jatavallabhula, M. Macklin, F. Golemo, V. Voleti, L. Petrini, M. Weiss, B. Considine, J. Parent-Levesque, K. Xie, K. Erleben, L. Paull, F. Shkurti, D. Nowrouzezahrai, and S. Fidler · 2021
Cited alongside, same era.
Nerf-vae: A geometry aware 3d scene generative model, 2021
A. R. Kosiorek, H. Strathmann, D. Zoran, P. Moreno, R. Schneider, S. Mokrá, and D. J. Rezende · 2021
Cited alongside, same era.
Giraffe: Representing scenes as compositional generative neural feature fields
M. Niemeyer and A. Geiger · 2021
Cited alongside, same era.
Learning object-compositional neural radiance field for editable scene rendering
B. Yang, Y. Zhang, Y. Xu, Y. Li, H. Zhou, H. Bao, G. Zhang, and Z. Cui · 2021
Cited alongside, same era.
Instant neural graphics primitives with a multiresolution hash encoding
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N. Hanselmann, K. Renz, K. Chitta, A. Bhattacharyya, and A. Geiger · 2022
Later among the works it cites.
Transfuser: Imitation with transformer-based sensor fusion for autonomous driving
K. Chitta, A. Prakash, B. Jaeger, Z. Yu, K. Renz, and A. Geiger · 2022
Later among the works it cites.
Block-nerf: Scalable large scene neural view synthesis
M. Tancik, V. Casser, X. Yan, S. Pradhan, B. Mildenhall, P. P. Srinivasan, J. T. Barron, and H. Kretzschmar · 2022
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Volumetric disentanglement for 3d scene manipulation, 2022
S. Benaim, F. Warburg, P. E. Christensen, and S. Belongie · 2022
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Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields, 2022
A. Mirzaei, T. Aumentado-Armstrong, K. G. Derpanis, J. Kelly, M. A. Brubaker, I. Gilitschenski, and A. Levinshtein · 2022
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Intrinsicnerf: Learning intrinsic neural radiance fields for editable novel view synthesis, 2022
W. Ye, S. Chen, C. Bao, H. Bao, M. Pollefeys, Z. Cui, and G. Zhang · 2022
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Y. Xu, M. Chai, Z. Shi, S. Peng, S. Ivan, S. Aliaksandr, C. Yang, Y. Shen, H.-Y. Lee, B. Zhou, and T. Sergy · 2022
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Panoptic neural fields: A semantic object-aware neural scene representation
A. Kundu, K. Genova, X. Yin, A. Fathi, C. Pantofaru, L. J. Guibas, A. Tagliasacchi, F. Dellaert, and T. Funkhouser · 2022
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Vision-only robot navigation in a neural radiance world
M. Adamkiewicz, T. Chen, A. Caccavale, R. Gardner, P. Culbertson, J. Bohg, and M. Schwager · 2022
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Differentiable physics simulation of dynamics-augmented neural objects, 2022
S. L. Cleac’h, H. Yu, M. Guo, T. A. Howell, R. Gao, J. Wu, Z. Manchester, and M. Schwager · 2022
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Learning multi-object dynamics with compositional neural radiance fields, 2022
D. Driess, Z. Huang, Y. Li, R. Tedrake, and M. Toussaint · 2022
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Objaverse: A universe of annotated 3d objects, 2022
M. Deitke, D. Schwenk, J. Salvador, L. Weihs, O. Michel, E. VanderBilt, L. Schmidt, K. Ehsani, A. Kembhavi, and A. Farhadi · 2022
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Do differentiable simulators give better policy gradients?, 2022
H. J. T. Suh, M. Simchowitz, K. Zhang, and R. Tedrake · 2022
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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
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Testing rare downstream safety violations via upstream adaptive sampling of perception error models
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S-neRF: Neural radiance fields for street views
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