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Closed-loop simulation environments play a crucial role in the validation and enhancement of autonomous driving systems (ADS).
ALVINN: An autonomous land vehicle in a neural network
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Design and use paradigms for gazebo, an open-source multi-robot simulator, in: IEEE/RSJ international conference on intelligent robots and systems (IROS)(IEEE Cat. No. 04CH37566), Ieee. pp. 2149–2154
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Cyberbotics ltd. webots™: professional mobile robot simulation
Michel, O., 2004 · 2004
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Usarsim: a robot simulator for research and education, in: IEEE International Conference on Robotics and Automation (ICRA), IEEE. pp. 1400–1405
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A review of traffic simulation software
Kotusevski, G., Hawick, K.A., 2009 · 2009
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Ros: an open-source robot operating system, in: ICRA workshop on open source software, Kobe, Japan. p. 5
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Microscopic traffic flow simulator VISSIM
Fellendorf, M., Vortisch, P., 2010 · 2010
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A survey of Monte Carlo tree search methods
Browne, C.B., Powley, E., Whitehouse, D., Lucas, S.M., Cowling, P.I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., Colton, S., 2012 · 2012
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Vision meets robotics: The KITTI dataset
Geiger, A., Lenz, P., Stiller, C., Urtasun, R., 2013 · 2013
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A history of the unity game engine
Haas, J.K., 2014 · 2014
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DeepDriving: Learning affordance for direct perception in autonomous driving, in: IEEE International Conference on Computer Vision (ICCV), pp. 2722–2730
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An introduction to Unreal Engine 4
Sanders, A., 2016 · 2016
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CARLA: An open urban driving simulator, in: Conference on Robot Learning (CoRL), PMLR. pp. 1–16
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V., 2017 · 2017
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1 year, 1000 km: The Oxford RobotCar dataset
Maddern, W., Pascoe, G., Linegar, C., Newman, P., 2017 · 2017
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End-to-end learning of driving models from large-scale video datasets, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2174–2182
Xu, H., Gao, Y., Yu, F., Darrell, T., 2017 · 2017
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Toward driving scene understanding: A dataset for learning driver behavior and causal reasoning, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7699–7707
Ramanishka, V., Chen, Y.T., Misu, T., Saenko, K., 2018 · 2018
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AirSim: High-fidelity visual and physical simulation for autonomous vehicles, in: Field and Service Robotics: Results of the 11th International Conference, Springer. pp. 621–635
Shah, S., Dey, D., Lovett, C., Kapoor, A., 2018 · 2018
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Towards corner case detection for autonomous driving, in: IEEE Intelligent vehicles symposium (IV), IEEE. pp. 438–445
Bolte, J.A., Bar, A., Lipinski, D., Fingscheidt, T., 2019 · 2019
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Exploring the limitations of behavior cloning for autonomous driving, in: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9329–9338
Codevilla, F., Santana, E., López, A.M., Gaidon, A., 2019 · 2019
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Is it safe to drive? an overview of factors, metrics, and datasets for driveability assessment in autonomous driving
Guo, J., Kurup, U., Shah, M., 2019 · 2019
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End-to-end interpretable neural motion planner, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8660–8669
Zeng, W., Luo, W., Suo, S., Sadat, A., Yang, B., Casas, S., Urtasun, R., 2019 · 2019
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nuScenes: A multimodal dataset for autonomous driving, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11621–11631
Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O., 2020 · 2020
Cited alongside, same era.
A survey on visual traffic simulation: Models, evaluations, and applications in autonomous driving, in: Computer Graphics Forum, Wiley Online Library. pp. 287–308
Chao, Q., Bi, H., Li, W., Mao, T., Wang, Z., Lin, M.C., Deng, Z., 2020 · 2020
Cited alongside, same era.
Deep multi-modal object detection and semantic segmentation for autonomous driving: Datasets, methods, and challenges
Feng, D., Haase-Schütz, C., Rosenbaum, L., Hertlein, H., Glaeser, C., Timm, F., Wiesbeck, W., Dietmayer, K., 2020 · 2020
Cited alongside, same era.
A survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., Macesanu, G., 2020 · 2020
Cited alongside, same era.
Deep learning-based vehicle behavior prediction for autonomous driving applications: A review
DriveLLM: Charting the path toward full autonomous driving with large language models
Cui, Y., Huang, S., Zhong, J., Liu, Z., Wang, Y., Sun, C., Li, B., Wang, X., Khajepour, A., 2023 · 2023
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Parting with misconceptions about learning-based vehicle motion planning, in: Conference on Robot Learning (CoRL), pp. 1268–1281
Dauner, D., Hallgarten, M., Geiger, A., Chitta, K., 2023 · 2023
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TrafficGen: Learning to generate diverse and realistic traffic scenarios, in: IEEE International Conference on Robotics and Automation (ICRA), IEEE. pp. 3567–3575
Feng, L., Li, Q., Peng, Z., Tan, S., Zhou, B., 2023 · 2023
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Avstack: An open-source, reconfigurable platform for autonomous vehicle development, in: International Conference on Cyber-Physical Systems (with CPS-IoT Week 2023), pp. 209–220
Hallyburton, R.S., Zhang, S., Pajic, M., 2023 · 2023
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Mozaffari, S., Al-Jarrah, O.Y., Dianati, M., Jennings, P., Mouzakitis, A., 2020 · 2020
Cited alongside, same era.
Lgsvl simulator: A high fidelity simulator for autonomous driving, in: IEEE International conference on intelligent transportation systems (ITSC), IEEE. pp. 1–6
Rong, G., Shin, B.H., Tabatabaee, H., Lu, Q., Lemke, S., Možeiko, M., Boise, E., Uhm, G., Gerow, M., Mehta, S., et al., 2020 · 2020
Cited alongside, same era.
Scalability in perception for autonomous driving: Waymo open dataset, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2446–2454
Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., Caine, B., et al., 2020 · 2020
Cited alongside, same era.
A survey of end-to-end driving: Architectures and training methods
Tampuu, A., Matiisen, T., Semikin, M., Fishman, D., Muhammad, N., 2020 · 2020
Cited alongside, same era.
A survey of autonomous driving: Common practices and emerging technologies
Yurtsever, E., Lambert, J., Carballo, A., Takeda, K., 2020 · 2020
Cited alongside, same era.
Autonomous driving architectures: insights of machine learning and deep learning algorithms
Bachute, M.R., Subhedar, J.M., 2021 · 2021
Cited alongside, same era.
SimNet: Learning reactive self-driving simulations from real-world observations, in: IEEE International Conference on Robotics and Automation (ICRA), IEEE. pp. 5119–5125
Bergamini, L., Ye, Y., Scheel, O., Chen, L., Hu, C., Del Pero, L., Osiński, B., Grimmett, H., Ondruska, P., 2021 · 2021
Cited alongside, same era.
nuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
Caesar, H., Kabzan, J., Tan, K.S., Fong, W.K., Wolff, E., Lang, A., Fletcher, L., Beijbom, O., Omari, S., 2021 · 2021
Cited alongside, same era.
Jin, Y., Shen, X., Peng, H., Liu, X., Qin, J., Li, J., Xie, J., Gao, P., Zhou, G., Gong, J., 2023 · 2023
Later among the works it cites.
Gpt-driver: Learning to drive with GPT
Mao, J., Qian, Y., Ye, J., Zhao, H., Wang, Y., 2023 · 2023
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From model-based to data-driven simulation: Challenges and trends in autonomous driving
Mütsch, F., Gremmelmaier, H., Becker, N., Bogdoll, D., Zofka, M.R., Zöllner, J.M., 2023 · 2023
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LanguageMPC: Large language models as decision makers for autonomous driving
Sha, H., Mu, Y., Jiang, Y., Chen, L., Xu, C., Luo, P., Li, S.E., Tomizuka, M., Zhan, W., Ding, M., 2023 · 2023
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Safety-enhanced autonomous driving using interpretable sensor fusion transformer, in: Conference on Robot Learning (CoRL), PMLR. pp. 726–737
Shao, H., Wang, L., Chen, R., Li, H., Liu, Y., 2023 · 2023
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The opencda open-source ecosystem for cooperative driving automation research
Xu, R., Xiang, H., Han, X., Xia, X., Meng, Z., Chen, C.J., Correa-Jullian, C., Ma, J., 2023 · 2023
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Aark: An open toolkit for autonomous racing research
Bockman, J., Howe, M., Orenstein, A., Dayoub, F., 2024 · 2024
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LimSim++: A closed-loop platform for deploying multimodal LLMs in autonomous driving
Fu, D., Lei, W., Wen, L., Cai, P., Mao, S., Dou, M., Shi, B., Qiao, Y., 2024 · 2024
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Waymax: An accelerated, data-driven simulator for large-scale autonomous driving research
Gulino, C., Fu, J., Luo, W., Tucker, G., Bronstein, E., Lu, Y., Harb, J., Pan, X., Wang, Y., Chen, X., et al., 2024 · 2024
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Bench2Drive: Towards multi-ability benchmarking of closed-loop end-to-end autonomous driving
Jia, X., Yang, Z., Li, Q., Zhang, Z., Yan, J., 2024 · 2024
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A survey on autonomous driving datasets: Statistics, annotation quality, and a future outlook
Liu, M., Yurtsever, E., Fossaert, J., Zhou, X., Zimmer, W., Cui, Y., Zagar, B.L., Knoll, A.C., 2024 · 2024
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Continuously learning, adapting, and improving: A dual-process approach to autonomous driving
Mei, J., Ma, Y., Yang, X., Wen, L., Cai, X., Li, X., Fu, D., Zhang, B., Cai, P., Dou, M., et al., 2024 · 2024
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One stack to rule them all: To drive automated vehicles, and reach for the 4th level
Ochs, S., Doll, J., Grimm, D., Fleck, T., Heinrich, M., Orf, S., Schotschneider, A., Gremmelmaier, H., Polley, R., Pavlitska, S., et al., 2024 · 2024
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Grounded relational inference: Domain knowledge driven explainable autonomous driving
Tang, C., Srishankar, N., Martin, S., Tomizuka, M., 2024 · 2024
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Robust platoon control of mixed autonomous and human-driven vehicles for obstacle collision avoidance: A cooperative sensing-based adaptive model predictive control approach
Tian, D., Zhou, J., Han, X., Lang, P., 2024 · 2024
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Driving into the future: Multiview visual forecasting and planning with world model for autonomous driving, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14749–14759
Wang, Y., He, J., Fan, L., Li, H., Chen, Y., Zhang, Z., 2024 · 2024
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DriveGPT4: Interpretable end-to-end autonomous driving via large language model
Xu, Z., Zhang, Y., Xie, E., Zhao, Z., Guo, Y., Wong, K.Y.K., Li, Z., Zhao, H., 2024 · 2024
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OASim: an open and adaptive simulator based on neural rendering for autonomous driving
Yan, G., Pi, J., Guo, J., Luo, Z., Dou, M., Deng, N., Huang, Q., Fu, D., Wen, L., Cai, P., et al., 2024 · 2024
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DriveArena: A closed-loop generative simulation platform for autonomous driving
Yang, X., Wen, L., Ma, Y., Mei, J., Li, X., Wei, T., Lei, W., Fu, D., Cai, P., Dou, M., et al., 2024 · 2024
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Autonomous driving system: A comprehensive survey
Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., Burke, A.F., 2024 · 2024
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NeuroNCAP: Photorealistic closed-loop safety testing for autonomous driving, in: European Conference on Computer Vision (ECCV), Springer. pp. 161–177
Ljungbergh, W., Tonderski, A., Johnander, J., Caesar, H., Åström, K., Felsberg, M., Petersson, C., 2025 · 2025
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GenAD: Generative end-to-end autonomous driving, in: European Conference on Computer Vision (ECCV), Springer. pp. 87–104
Zheng, W., Song, R., Guo, X., Zhang, C., Chen, L., 2025 · 2025
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