Fetching the paper…
Reading the bibliography…
Autonomous driving systems have witnessed a significant development during the past years thanks to the advance in machine learning-enabled sensing and decision-making algorithms.
Z. Huang, M. Arief, H. Lam, and D. Zhao, “Evaluation uncertainty in data-driven self-driving testing,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 1902–1907
1907
Earlier work this paper cites.
1909
Earlier work this paper cites.
J. Bock, R. Krajewski, T. Moers, S. Runde, L. Vater, and L. Eckstein, “The ind dataset: A drone dataset of naturalistic road user trajectories at german intersections,” in 2020 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2020, pp. 1929–1934
1934
Earlier work this paper cites.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International conference on machine learning . PMLR, 2016, pp. 1928–1937
1937
Earlier work this paper cites.
S. Kullback and R. A. Leibler, “On information and sufficiency,” The annals of mathematical statistics , vol. 22, no. 1, pp. 79–86, 1951
1951
Earlier work this paper cites.
L. V. Kantorovich, “Mathematical methods of organizing and planning production,” Management science , vol. 6, no. 4, pp. 366–422, 1960
1960
Earlier work this paper cites.
J. C. Hayward, “Near miss determination through use of a scale of danger,” 1972
1972
Earlier work this paper cites.
B. L. Allen, B. T. Shin, and P. J. Cooper, “Analysis of traffic conflicts and collisions,” Tech. Rep., 1978
1978
Earlier work this paper cites.
S. Almqvist, C. Hyden, and R. Risser, “Use of speed limiters in cars for increased safety and a better environment,” Transportation Research Record , no. 1318, 1991
1991
Earlier work this paper cites.
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning , vol. 8, no. 3, pp. 229–256, 1992
1992
Earlier work this paper cites.
R. M. Smullyan, First-order logic . Courier Corporation, 1995
1995
Earlier work this paper cites.
Z. Ghahramani, “Learning dynamic bayesian networks,” International School on Neural Networks, Initiated by IIASS and EMFCSC , pp. 168–197, 1997
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
A. v. Lamsweerde, “Formal specification: a roadmap,” in Proceedings of the Conference on the Future of Software Engineering , 2000, pp. 147–159
2000
Earlier work this paper cites.
B. Wymann, E. Espié, C. Guionneau, C. Dimitrakakis, R. Coulom, and A. Sumner, “Torcs, the open racing car simulator,” Software available at http://torcs. sourceforge. net , vol. 4, no. 6, p. 2, 2000
2000
Earlier work this paper cites.
M. M. Minderhoud and P. H. Bovy, “Extended time-to-collision measures for road traffic safety assessment,” Accident Analysis & Prevention , vol. 33, no. 1, pp. 89–97, 2001
2001
Earlier work this paper cites.
S. G. Henderson, “Input model uncertainty: Why do we care and what should we do about it?” in Winter Simulation Conference , vol. 1, 2003, pp. 90–100
2003
Earlier work this paper cites.
H. Pham and X. Zhang, “Nhpp software reliability and cost models with testing coverage,” European Journal of Operational Research , vol. 145, no. 2, pp. 443–454, 2003
2003
Earlier work this paper cites.
O. Michel, “Webots: Professional mobile robot simulation,” Journal of Advanced Robotics Systems , vol. 1, no. 1, pp. 39–42, 2004. [Online]. Available: http://www.ars-journal.com/International-Journal-of-Advanced-Robotic-Systems/Volume-1/39-42.pdf
2004
Earlier work this paper cites.
M. Goslin and M. R. Mine, “The panda3d graphics engine,” Computer , vol. 37, no. 10, pp. 112–114, 2004
2004
Earlier work this paper cites.
Z. Huang, M. Arief, H. Lam, and D. Zhao, “Synthesis of different autonomous vehicles test approaches,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 2000–2005
2005
Earlier work this paper cites.
W. G. Najm, J. D. Smith, M. Yanagisawa et al. , “Pre-crash scenario typology for crash avoidance research,” United States. National Highway Traffic Safety Administration, Tech. Rep., 2007
2007
Earlier work this paper cites.
H.-G. Beyer and B. Sendhoff, “Robust optimization–a comprehensive survey,” Computer methods in applied mechanics and engineering , vol. 196, no. 33-34, pp. 3190–3218, 2007
2007
Earlier work this paper cites.
K. Ozbay, H. Yang, B. Bartin, and S. Mudigonda, “Derivation and validation of new simulation-based surrogate safety measure,” Transportation research record , vol. 2083, no. 1, pp. 105–113, 2008
2008
Earlier work this paper cites.
E. B. Fox, E. B. Sudderth, M. I. Jordan, and A. S. Willsky, “An hdp-hmm for systems with state persistence,” in Proceedings of the 25th international conference on Machine learning , 2008, pp. 312–319
2008
Earlier work this paper cites.
G. J. Brostow, J. Fauqueur, and R. Cipolla, “Semantic object classes in video: A high-definition ground truth database,” Pattern Recognition Letters , vol. 30, no. 2, pp. 88–97, 2009
2009
Earlier work this paper cites.
J.-M. Jullien, C. Martel, L. Vignollet, and M. Wentland, “Openscenario: a flexible integrated environment to develop educational activities based on pedagogical scenarios,” in 2009 Ninth IEEE International Conference on Advanced Learning Technologies . IEEE, 2009, pp. 509–513
2009
Earlier work this paper cites.
Z. Saquib, N. Salam, R. P. Nair, N. Pandey, and A. Joshi, “A survey on automatic speaker recognition systems,” Signal Processing and Multimedia , pp. 134–145, 2010
2010
Earlier work this paper cites.
R. R. Barton, B. L. Nelson, and W. Xie, “A framework for input uncertainty analysis,” in Proceedings of the 2010 Winter Simulation Conference . IEEE, 2010, pp. 1189–1198
2010
Earlier work this paper cites.
J. Bennett, OpenStreetMap . Packt Publishing Ltd, 2010
2010
Earlier work this paper cites.
M. Arief, P. Glynn, and D. Zhao, “An accelerated approach to safely and efficiently test pre-production autonomous vehicles on public streets,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 2006–2011
2011
Earlier work this paper cites.
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
R. van der Made, M. Tideman, U. Lages, R. Katz, and M. Spencer, “Automated generation of virtual driving scenarios from test drive data,” in 24th International Technical Conference on the Enhanced Safety of Vehicles (ESV) National Highway Traffic Safety Administration , no. 15-0268, 2015
2015
Earlier work this paper cites.
T. A. Wheeler, M. J. Kochenderfer, and P. Robbel, “Initial scene configurations for highway traffic propagation,” in 2015 IEEE 18th International Conference on Intelligent Transportation Systems . IEEE, 2015, pp. 279–284
2015
Earlier work this paper cites.
R. Lee, M. J. Kochenderfer, O. J. Mengshoel, G. P. Brat, and M. P. Owen, “Adaptive stress testing of airborne collision avoidance systems,” in 2015 IEEE/AIAA 34th Digital Avionics Systems Conference (DASC) . IEEE, 2015, pp. 6C2–1
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. Zhao, H. Peng, H. Lam, S. Bao, K. Nobukawa, D. J. LeBlanc, and C. S. Pan, “Accelerated evaluation of automated vehicles in lane change scenarios,” in ASME 2015 Dynamic Systems and Control Conference . American Society of Mechanical Engineers, 2015, pp. V001T17A002—-V001T17A002
2015
Earlier work this paper cites.
K. Sohn, H. Lee, and X. Yan, “Learning structured output representation using deep conditional generative models,” Advances in neural information processing systems , vol. 28, pp. 3483–3491, 2015
2015
Earlier work this paper cites.
T. A. Wheeler and M. J. Kochenderfer, “Factor graph scene distributions for automotive safety analysis,” in 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2016, pp. 1035–1040
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” Advances in neural information processing systems , vol. 29, pp. 4565–4573, 2016
2016
Earlier work this paper cites.
A. Van Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” in International Conference on Machine Learning . PMLR, 2016, pp. 1747–1756
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
X. Li, F. Flohr, Y. Yang, H. Xiong, M. Braun, S. Pan, K. Li, and D. M. Gavrila, “A new benchmark for vision-based cyclist detection,” in 2016 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2016, pp. 1028–1033
2016
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 3213–3223
2016
Earlier work this paper cites.
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez, “The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 3234–3243
2016
Earlier work this paper cites.
A. Robicquet, A. Sadeghian, A. Alahi, and S. Savarese, “Learning social etiquette: Human trajectory understanding in crowded scenes,” in European conference on computer vision . Springer, 2016, pp. 549–565
2016
Earlier work this paper cites.
“Openai gym car racing,” 2016. [Online]. Available: https://gym.openai.com/envs/CarRacing-v0/
2016
Earlier work this paper cites.
P. Spirtes and K. Zhang, “Causal discovery and inference: concepts and recent methodological advances,” in Applied informatics , vol. 3, no. 1. SpringerOpen, 2016, pp. 1–28
2016
Earlier work this paper cites.
E. Rocklage, H. Kraft, A. Karatas, and J. Seewig, “Automated scenario generation for regression testing of autonomous vehicles,” in 2017 ieee 20th international conference on intelligent transportation systems (itsc) . IEEE, 2017, pp. 476–483
2017
Earlier work this paper cites.
S. S. Mahmud, L. Ferreira, M. S. Hoque, and A. Tavassoli, “Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs,” IATSS research , vol. 41, no. 4, pp. 153–163, 2017
2017
Earlier work this paper cites.
A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne, “Imitation learning: A survey of learning methods,” ACM Computing Surveys (CSUR) , vol. 50, no. 2, pp. 1–35, 2017
2017
Earlier work this paper cites.
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 23–30
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Z. Huang, D. Zhao, H. Lam, D. J. LeBlanc, and H. Peng, “Evaluation of automated vehicles in the frontal cut-in scenario — an enhanced approach using piecewise mixture models,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) , May 2017, pp. 197–202
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16
2017
Earlier work this paper cites.
W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 Year, 1000km: The Oxford RobotCar Dataset,” The International Journal of Robotics Research (IJRR) , vol. 36, no. 1, pp. 3–15, 2017. [Online]. Available: http://dx.doi.org/10.1177/0278364916679498
2017
Earlier work this paper cites.
G. Neuhold, T. Ollmann, S. Rota Bulo, and P. Kontschieder, “The mapillary vistas dataset for semantic understanding of street scenes,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 4990–4999
2017
Earlier work this paper cites.
S. R. Richter, Z. Hayder, and V. Koltun, “Playing for benchmarks,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 2213–2222
2017
Earlier work this paper cites.
C. L. Azevedo, N. M. Deshmukh, B. Marimuthu, S. Oh, K. Marczuk, H. Soh, K. Basak, T. Toledo, L.-S. Peh, and M. E. Ben-Akiva, “Simmobility short-term: An integrated microscopic mobility simulator,” Transportation Research Record , vol. 2622, no. 1, pp. 13–23, 2017
2017
Earlier work this paper cites.
N. Papernot, P. McDaniel, I. Goodfellow, S. Jha, Z. B. Celik, and A. Swami, “Practical black-box attacks against machine learning,” in Proceedings of the 2017 ACM on Asia conference on computer and communications security , 2017, pp. 506–519
2017
Earlier work this paper cites.
I. Masi, Y. Wu, T. Hassner, and P. Natarajan, “Deep face recognition: A survey,” in 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI) . IEEE, 2018, pp. 471–478
2018
Earlier work this paper cites.
E. Thorn, S. C. Kimmel, M. Chaka, B. A. Hamilton et al. , “A framework for automated driving system testable cases and scenarios,” United States. Department of Transportation. National Highway Traffic Safety Administration, Tech. Rep., 2018
2018
Earlier work this paper cites.
K. Eykholt, I. Evtimov, E. Fernandes, B. Li, A. Rahmati, C. Xiao, A. Prakash, T. Kohno, and D. Song, “Robust physical-world attacks on deep learning visual classification,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 1625–1634
2018
Earlier work this paper cites.
T. Menzel, G. Bagschik, and M. Maurer, “Scenarios for development, test and validation of automated vehicles,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1821–1827
2018
Earlier work this paper cites.
F. Kruber, J. Wurst, and M. Botsch, “An unsupervised random forest clustering technique for automatic traffic scenario categorization,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2018, pp. 2811–2818
2018
Cited alongside, same era.
W. Wang and D. Zhao, “Extracting traffic primitives directly from naturalistically logged data for self-driving applications,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 1223–1229, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. Devaranjan, A. Kar, and S. Fidler, “Meta-sim2: Unsupervised learning of scene structure for synthetic data generation,” in European Conference on Computer Vision . Springer, 2020, pp. 715–733
2020
Later among the works it cites.
W. Ding, M. Xu, and D. Zhao, “Cmts: A conditional multiple trajectory synthesizer for generating safety-critical driving scenarios,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 4314–4321
2020
Later among the works it cites.
M. Wen, J. Park, and K. Cho, “A scenario generation pipeline for autonomous vehicle simulators,” Human-centric Computing and Information Sciences , vol. 10, pp. 1–15, 2020
2020
Later among the works it cites.
J. Tu, M. Ren, S. Manivasagam, M. Liang, B. Yang, R. Du, F. Cheng, and R. Urtasun, “Physically realizable adversarial examples for lidar object detection,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 13 716–13 725
2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
M. Koren, S. Alsaif, R. Lee, and M. J. Kochenderfer, “Adaptive stress testing for autonomous vehicles,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1–7
2018
Cited alongside, same era.
G. Bagschik, T. Menzel, and M. Maurer, “Ontology based scene creation for the development of automated vehicles,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1813–1820
2018
Cited alongside, same era.
M. Althoff and S. Lutz, “Automatic generation of safety-critical test scenarios for collision avoidance of road vehicles,” in 2018 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2018, pp. 1326–1333
2018
Cited alongside, same era.
H. Kato, Y. Ushiku, and T. Harada, “Neural 3d mesh renderer,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 3907–3916
2018
Cited alongside, same era.
S. Shafaei, S. Kugele, M. H. Osman, and A. Knoll, “Uncertainty in machine learning: A safety perspective on autonomous driving,” in International Conference on Computer Safety, Reliability, and Security . Springer, 2018, pp. 458–464
2018
Cited alongside, same era.
Later among the works it cites.
——, “Adaptive stress testing without domain heuristics using go-explore,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2020, pp. 1–6
2020
Later among the works it cites.
S. Kuutti, S. Fallah, and R. Bowden, “Training adversarial agents to exploit weaknesses in deep control policies,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 108–114
2020
Later among the works it cites.
P. Cai, Y. Lee, Y. Luo, and D. Hsu, “Summit: A simulator for urban driving in massive mixed traffic,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 4023–4029
2020
Later among the works it cites.
D. J. Fremont, E. Kim, Y. V. Pant, S. A. Seshia, A. Acharya, X. Bruso, P. Wells, S. Lemke, Q. Lu, and S. Mehta, “Formal scenario-based testing of autonomous vehicles: From simulation to the real world,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2020, pp. 1–8
2020
Later among the works it cites.
2020
Later among the works it cites.
M. Klischat, E. I. Liu, F. Holtke, and M. Althoff, “Scenario factory: Creating safety-critical traffic scenarios for automated vehicles,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2020, pp. 1–7
2020
Later among the works it cites.
S. Shiroshita, S. Maruyama, D. Nishiyama, M. Y. Castro, K. Hamzaoui, G. Rosman, J. DeCastro, K.-H. Lee, and A. Gaidon, “Behaviorally diverse traffic simulation via reinforcement learning,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 2103–2110
2020
Later among the works it cites.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 8110–8119
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell, “Bdd100k: A diverse driving dataset for heterogeneous multitask learning,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 2636–2645
2020
Later among the works it cites.
R. Krajewski, T. Moers, J. Bock, L. Vater, and L. Eckstein, “The round dataset: A drone dataset of road user trajectories at roundabouts in germany,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2020, pp. 1–6
2020
Later among the works it cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 621–11 631
2020
Later among the works it cites.
2020
Later among the works it cites.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine et al. , “Scalability in perception for autonomous driving: Waymo open dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2446–2454
2020
Later among the works it cites.
Q.-H. Pham, P. Sevestre, R. S. Pahwa, H. Zhan, C. H. Pang, Y. Chen, A. Mustafa, V. Chandrasekhar, and J. Lin, “A* 3d dataset: Towards autonomous driving in challenging environments,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 2267–2273
2020
Later among the works it cites.
W. Schwarting, T. Seyde, I. Gilitschenski, L. Liebenwein, R. Sander, S. Karaman, and D. Rus, “Deep latent competition: Learning to race using visual control policies in latent space,” in Conference on Robot Learning , 2020
2020
Later among the works it cites.
G. Rong, B. H. Shin, H. Tabatabaee, Q. Lu, S. Lemke, M. Možeiko, E. Boise, G. Uhm, M. Gerow, S. Mehta et al. , “Lgsvl simulator: A high fidelity simulator for autonomous driving,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2020, pp. 1–6
2020
Later among the works it cites.
“SMARTS Scenario Studio,” https://github.com/huawei-noah/SMARTS/tree/master/smarts/sstudio , 2020
2020
Later among the works it cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in European conference on computer vision . Springer, 2020, pp. 405–421
2020
Later among the works it cites.
2021
Later among the works it cites.
J. M. Scanlon, K. D. Kusano, T. Daniel, C. Alderson, A. Ogle, and T. Victor, “Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain,” 2021
2021
Later among the works it cites.
Y. Chen, F. Rong, S. Duggal, S. Wang, X. Yan, S. Manivasagam, S. Xue, E. Yumer, and R. Urtasun, “Geosim: Realistic video simulation via geometry-aware composition for self-driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7230–7240
2021
Later among the works it cites.
2021
Later among the works it cites.
A. Savkin, R. Ellouze, N. Navab, and F. Tombari, “Unsupervised traffic scene generation with synthetic 3d scene graphs,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1229–1235
2021
Later among the works it cites.
M. Håkansson and J. Wall, “Driving scenario generation using generative adversarial networks,” Master Thesis , 2021
2021
Later among the works it cites.
S. Tan, K. Wong, S. Wang, S. Manivasagam, M. Ren, and R. Urtasun, “Scenegen: Learning to generate realistic traffic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 892–901
2021
Later among the works it cites.
2021
Later among the works it cites.
Y. Zhu, C. Miao, F. Hajiaghajani, M. Huai, L. Su, and C. Qiao, “Adversarial attacks against lidar semantic segmentation in autonomous driving,” in Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems , 2021, pp. 329–342
2021
Later among the works it cites.
W. Ding, B. Chen, B. Li, K. J. Eun, and D. Zhao, “Multimodal safety-critical scenarios generation for decision-making algorithms evaluation,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 1551–1558, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Wang, A. Pun, J. Tu, S. Manivasagam, A. Sadat, S. Casas, M. Ren, and R. Urtasun, “Advsim: Generating safety-critical scenarios for self-driving vehicles,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9909–9918
2021
Later among the works it cites.
S. Feng, X. Yan, H. Sun, Y. Feng, and H. X. Liu, “Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment,” Nature communications , vol. 12, no. 1, pp. 1–14, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Arief, Z. Huang, G. K. S. Kumar, Y. Bai, S. He, W. Ding, H. Lam, and D. Zhao, “Deep probabilistic accelerated evaluation: A robust certifiable rare-event simulation methodology for black-box safety-critical systems,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2021, pp. 595–603
2021
Later among the works it cites.
2021
Later among the works it cites.
B. Chen, X. Chen, Q. Wu, and L. Li, “Adversarial evaluation of autonomous vehicles in lane-change scenarios,” IEEE Transactions on Intelligent Transportation Systems , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
X. Wang, H. Krasowski, and M. Althoff, “Commonroad-rl: a configurable reinforcement learning environment for motion planning of autonomous vehicles,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 466–472
2021
Later among the works it cites.
2021
Later among the works it cites.
S. Suo, S. Regalado, S. Casas, and R. Urtasun, “Trafficsim: Learning to simulate realistic multi-agent behaviors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 400–10 409
2021
Later among the works it cites.
D. Fernández Llorca and E. Gómez, “Trustworthy autonomous vehicles,” Joint Research Centre (Seville site), Tech. Rep., 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
D. drive Contributors, “DI-drive: OpenDILab decision intelligence platform for autonomous driving simulation,” https://github.com/opendilab/DI-drive , 2021
2021
Later among the works it cites.
B. Wilson, W. Qi, T. Agarwal, J. Lambert, J. Singh, S. Khandelwal, B. Pan, R. Kumar, A. Hartnett, J. K. Pontes et al. , “Argoverse 2: Next generation datasets for self-driving perception and forecasting,” 2021
2021
Later among the works it cites.
P. Xiao, Z. Shao, S. Hao, Z. Zhang, X. Chai, J. Jiao, Z. Li, J. Wu, K. Sun, K. Jiang et al. , “Pandaset: Advanced sensor suite dataset for autonomous driving,” in 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) . IEEE, 2021, pp. 3095–3101
2021
Later among the works it cites.
2021
Later among the works it cites.
J. Herman, J. Francis, S. Ganju, B. Chen, A. Koul, A. Gupta, A. Skabelkin, I. Zhukov, M. Kumskoy, and E. Nyberg, “Learn-to-race: A multimodal control environment for autonomous racing,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 9793–9802
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
“California Department of Motor Vehicle Disengagement Report,” https://www.dmv.ca.gov/portal/vehicle-industry-services/autonomous-vehicles/disengagement-reports/ , 2022, [Online]
2022
Closest in time.
D. Rempe, J. Philion, L. J. Guibas, S. Fidler, and O. Litany, “Generating useful accident-prone driving scenarios via a learned traffic prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 17 305–17 315
2022
Closest in time.
D. J. Fremont, E. Kim, T. Dreossi, S. Ghosh, X. Yue, A. L. Sangiovanni-Vincentelli, and S. A. Seshia, “Scenic: A language for scenario specification and data generation,” Machine Learning , pp. 1–45, 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
C. Xu, W. Ding, W. Lyu, Z. Liu, S. Wang, Y. He, H. Hu, D. Zhao, and B. Li, “Safebench: A benchmarking platform for safety evaluation of autonomous vehicles,” in Advances in Neural Information Processing Systems , 2022
2022
Closest in time.
2022
Closest in time.