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Recent advances in autonomous system simulation platforms have significantly enhanced the safe and scalable testing of driving policies.
M. Treiber, A. Hennecke, and D. Helbing, “Congested traffic states in empirical observations and microscopic simulations,” Phys. Rev. E , vol. 62, no. 2, p. 1805, 2000
2000
Earlier work this paper cites.
A. Kesting, M. Treiber, and D. Helbing, “General lane-changing model mobil for car-following models,” Transp. Res. Rec. , vol. 1999, no. 1, pp. 86–94, 2007
2007
Earlier work this paper cites.
N. Kalra and S. M. Paddock, “Driving to safety: How many miles of driving would it take to demonstrate autonomous vehicle reliability?” Transp. Res. Part A: Policy Pract. , vol. 94, pp. 182–193, 2016
2016
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Proc. Conf. Robot Learn. PMLR, 2017, pp. 1–16
2017
Earlier work this paper cites.
H. Xu, Y. Gao, F. Yu, and T. Darrell, “End-to-end learning of driving models from large-scale video datasets,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2017, pp. 2174–2182
2017
Earlier work this paper cites.
S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” in Proc. Int. Conf. Field Serv. Robot. Springer, 2018, pp. 621–635
2018
Earlier work this paper cites.
P. A. Lopez, M. Behrisch, L. Bieker-Walz, J. Erdmann, Y.-P. Flötteröd, R. Hilbrich, L. Lücken, J. Rummel, P. Wagner, and E. Wießner, “Microscopic traffic simulation using sumo,” in 2018 21st international conference on intelligent transportation systems (ITSC) . Ieee, 2018, pp. 2575–2582
2018
Earlier work this paper cites.
E. Leurent, “An environment for autonomous driving decision-making,” 2018. [Online]. Available: https://github.com/eleurent/highway-env
2018
Earlier work this paper cites.
M. Müller, V. Casser, J. Lahoud, N. Smith, and B. Ghanem, “Sim4cv: A photo-realistic simulator for computer vision applications,” Int. J. Comput. Vis. , vol. 126, pp. 902–919, 2018
2018
Earlier work this paper cites.
D. Team, “Deepdrive: a simulator that allows anyone with a pc to push the state-of-the-art in self-driving,” 2019
2019
Earlier work this paper cites.
H. Zhang, S. Feng, C. Liu, Y. Ding, Y. Zhu, Z. Zhou, W. Zhang, Y. Yu, H. Jin, and Z. Li, “Cityflow: A multi-agent reinforcement learning environment for large scale city traffic scenario,” in Proc. World Wide Web Conf. , 2019, pp. 3620–3624
2019
Earlier work this paper cites.
2019
Earlier work this paper 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 Proc. IEEE Int. Conf. Intell. Transp. Syst. (ITSC) . IEEE, 2020, pp. 1–6
2020
Earlier work this paper cites.
P. Cai, Y. Lee, Y. Luo, and D. Hsu, “Summit: A simulator for urban driving in massive mixed traffic,” in Proc. IEEE Int. Conf. Robot. Autom. IEEE, 2020, pp. 4023–4029
2020
Earlier work this paper cites.
P. Palanisamy, “Multi-agent connected autonomous driving using deep reinforcement learning,” in Proc. Int. Joint Conf. Neural Netw. IEEE, 2020, pp. 1–7
2020
Earlier work this paper cites.
X. Zhou, V. Koltun, and P. Krähenbühl, “Tracking objects as points,” in Proc. Eur. Conf. Comput. Vis. Springer, 2020, pp. 474–490
2020
Earlier work this paper cites.
W. Bao, Q. Yu, and Y. Kong, “Uncertainty-based traffic accident anticipation with spatio-temporal relational learning,” in Proc. ACM Multimedia Conf. , May 2020
2020
Earlier work this paper cites.
“Study on enhancement of 3GPP Support for 5G V2X Services,” 3rd Generation Partnership Project (3GPP), Technical Report TR 22.886, 2020, available at: https://www.3gpp.org/ftp/Specs/archive/22_series/22.886/
2020
Earlier work this paper cites.
M. Zhou, J. Luo, J. Villella, Y. Yang, D. Rusu, J. Miao, W. Zhang, M. Alban, I. Fadakar, Z. Chen et al. , “Smarts: An open-source scalable multi-agent rl training school for autonomous driving,” in Proc. Conf. Robot Learn. PMLR, 2021, pp. 264–285
2021
Earlier work this paper cites.
C. Wu, A. R. Kreidieh, K. Parvate, E. Vinitsky, and A. M. Bayen, “Flow: A modular learning framework for mixed autonomy traffic,” IEEE Trans. Robot. , vol. 38, no. 2, pp. 1270–1286, 2021
2021
Earlier work this paper cites.
Q. Li, Z. Peng, L. Feng, Q. Zhang, Z. Xue, and B. Zhou, “Metadrive: Composing diverse driving scenarios for generalizable reinforcement learning,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 45, no. 3, pp. 3461–3475, 2022
2022
Earlier work this paper cites.
E. Vinitsky, N. Lichtlé, X. Yang, B. Amos, and J. Foerster, “Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world,” Adv. Neural Inf. Process. Syst. , vol. 35, pp. 3962–3974, 2022
2022
Earlier work this paper cites.
W. Wang, L. Wang, C. Zhang, C. Liu, L. Sun et al. , “Social interactions for autonomous driving: A review and perspectives,” Found. Trends Robot. , vol. 10, no. 3-4, pp. 198–376, 2022
2022
Earlier work this paper cites.
A. Amini, T.-H. Wang, I. Gilitschenski, W. Schwarting, Z. Liu, S. Han, S. Karaman, and D. Rus, “Vista 2.0: An open, data-driven simulator for multimodal sensing and policy learning for autonomous vehicles,” in Proc. IEEE Int. Conf. Robot. Autom. IEEE, 2022, pp. 2419–2426
2022
Earlier work this paper cites.
Y. Luo, P. Cai, Y. Lee, and D. Hsu, “Gamma: A general agent motion model for autonomous driving,” IEEE Robot. Autom. Lett. , vol. 7, no. 2, pp. 3499–3506, 2022
2022
Earlier work this paper cites.
2022
Cited alongside, same era.
Y. Yao, X. Wang, M. Xu, Z. Pu, Y. Wang, E. Atkins, and D. Crandall, “Dota: unsupervised detection of traffic anomaly in driving videos,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
2022
Cited alongside, same era.
S. Chen, S. Zong, T. Chen, Z. Huang, Y. Chen, and S. Labi, “A taxonomy for autonomous vehicles considering ambient road infrastructure,” Sustainability , vol. 15, no. 14, p. 11258, 2023
2023
Cited alongside, same era.
J. Liu, D. Zhou, P. Hang, Y. Ni, and J. Sun, “Towards socially responsive autonomous vehicles: A reinforcement learning framework with driving priors and coordination awareness,” IEEE Transactions on Intelligent Vehicles , vol. 9, no. 1, pp. 827–838, 2023
2023
Cited alongside, same era.
Y. Li, W. Yuan, S. Zhang, W. Yan, Q. Shen, C. Wang, and M. Yang, “Choose your simulator wisely: A review on open-source simulators for autonomous driving,” IEEE Trans. Intell. Veh. , 2024
2024
Later among the works it cites.
Nvidia, “Nvidia Drive End-to-End Platform for Software-Defined Vehicles,” Online, 2024, accessed: Mar. 12, 2024. [Online]. Available: https://www.nvidia.com/en-us/self-driving-cars/
2024
Later among the works it cites.
rFpro, “The World’s Most Accurate Simulation Environment,” Online, 2023, accessed: Mar. 12, 2024. [Online]. Available: https://rfpro.com
2024
Later among the works it cites.
B. Wymann, E. Espié, C. Guionneau, C. Dimitrakakis, R. Coulom, and A. Sumner, “TORCS, the open racing car simulator,” 2020, accessed: Mar. 12, 2024. [Online]. Available: https://sourceforge.net/projects/torcs/
2024
Later among the works it cites.
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S. Feng, H. Sun, X. Yan, H. Zhu, Z. Zou, S. Shen, and H. X. Liu, “Dense reinforcement learning for safety validation of autonomous vehicles,” Nature , vol. 615, no. 7953, pp. 620–627, 2023
2023
Cited alongside, same era.
C. Gulino, J. Fu, W. Luo, G. Tucker, E. Bronstein, Y. Lu, J. Harb, X. Pan, Y. Wang, X. Chen et al. , “Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research,” Adv. Neural Inf. Process. Syst. , vol. 36, pp. 7730–7742, 2023
2023
Cited alongside, same era.
Q. Li, Z. M. Peng, L. Feng, Z. Liu, C. Duan, W. Mo, and B. Zhou, “Scenarionet: Open-source platform for large-scale traffic scenario simulation and modeling,” Adv. Neural Inf. Process. Syst. , vol. 36, pp. 3894–3920, 2023
2023
Cited alongside, same era.
OpenAI, “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
2023
Cited alongside, same era.
L. Zhou, Y. Song, Y. Gao, Z. Yu, M. Sodamin, H. Liu, L. Ma, L. Liu, H. Liu, Y. Liu et al. , “Garchingsim: An autonomous driving simulator with photorealistic scenes and minimalist workflow,” in Proc. IEEE Int. Conf. Intell. Transp. Syst. (ITSC) . IEEE, 2023, pp. 4227–4232
2023
Cited alongside, same era.
Wayve, “LINGO-1: Exploring Natural Language for Autonomous Driving,” 2023, https://wayve.ai/thinking/lingo-natural-language-autonomous-driving/
2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” in Proc. Int. Conf. Mach. Learn. PMLR, 2023, pp. 28 492–28 518
2023
Cited alongside, same era.
Z. Ma, Q. Sun, and T. Matsumaru, “Bidirectional planning for autonomous driving framework with large language model,” Sensors , vol. 24, no. 20, p. 6723, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Sheng, Z. Huang, and S. Chen, “Traffic expertise meets residual rl: Knowledge-informed model-based residual reinforcement learning for cav trajectory control,” Commun. Transp. Res. , vol. 4, p. 100142, 2024
2024
Later among the works it cites.
——, “Ego-planning-guided multi-graph convolutional network for heterogeneous agent trajectory prediction,” Comput.-Aided Civ. Infrastruct. Eng. , vol. 39, no. 22, pp. 3357–3374, 2024
2024
Later among the works it cites.
H. Shao, Y. Hu, L. Wang, G. Song, S. L. Waslander, Y. Liu, and H. Li, “Lmdrive: Closed-loop end-to-end driving with large language models,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. , 2024, pp. 15 120–15 130
2024
Later among the works it cites.
2024
Later among the works it cites.
Z. Huang, S. Chen, Y. Pian, Z. Sheng, S. Ahn, and D. A. Noyce, “Toward c-v2x enabled connected transportation system: Rsu-based cooperative localization framework for autonomous vehicles,” IEEE Trans. Intell. Transp. Syst. , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
Mechanical Simulation. (2025) Carsim. [Online]. Available: https://www.carsim.com
2025
Closest in time.
I. Automotive. (2025) Carmaker. [Online]. Available: https://ipg-automotive.com/products-services/simulation-software/carmaker/
2025
Closest in time.
O. Robotics. (2025) Gazebo. [Online]. Available: https://gazebosim.org/
2025
Closest in time.
Mathworks. (2025) Vehicle dynamics blockset. [Online]. Available: https://www.mathworks.com/products/vehicle-dynamics.html
2025
Closest in time.
NVIDIA Corporation, “NVIDIA DRIVE Sim,” 2025, https://developer.nvidia.com/drive/simulation
2025
Closest in time.
Applied Intuition, Inc., “Applied Intuition,” 2025, https://www.appliedintuition.com/
2025
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MORAI Inc., “MORAI Inc.” 2025, https://www.morai.ai/
2025
Closest in time.
K. Long, Z. Sheng, H. Shi, X. Li, S. Chen, and S. Ahn, “A physics enhanced residual learning (perl) framework for vehicle trajectory prediction,” Commun. Transp. Res. , 2025
2025
Closest in time.
Z. Sheng, Z. Huang, Y. Qu, Y. Leng, and S. Chen, “Talk2traffic: Interactive and editable traffic scenario generation for autonomous driving with multimodal large language model,” in Proc. IEEE/CVF Conf. Comput. Vis. Pattern Recognit. (CVPR) Workshops , 2025
2025
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2025
Closest in time.
Z. Xu, T. Chen, Z. Huang, Y. Xing, and S. Chen, “Personalizing driver agent using large language models for driving safety and smarter human–machine interactions,” IEEE Intell. Transp. Syst. Mag. , 2025
2025
Closest in time.