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End-To-End Planning of Autonomous Driving in Industry and Academia: 2022-2023 (2401.08658v2)

Published 26 Dec 2023 in cs.RO and cs.AI

Abstract: This paper aims to provide a quick review of the methods including the technologies in detail that are currently reported in industry and academia. Specifically, this paper reviews the end-to-end planning, including Tesla FSD V12, Momenta 2023, Horizon Robotics 2023, Motional RoboTaxi 2022, Woven Planet (Toyota): Urban Driver, and Nvidia. In addition, we review the state-of-the-art academic studies that investigate end-to-end planning of autonomous driving. This paper provides readers with a concise structure and fast learning of state-of-the-art end-to-end planning for 2022-2023. This article provides a meaningful overview as introductory material for beginners to follow the state-of-the-art end-to-end planning of autonomous driving in industry and academia, as well as supplementary material for advanced researchers.

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References (12)
  1. U. NHTSA, “Critical reasons for crashes investigated in the national motor vehicle crash causation survey,” DOT HS 812 115. Washington, DC: National Highway Traffic Safety Administration (NHTSA), US Department of Transportation, 2015.
  2. D. J. Fagnant and K. Kockelman, “Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations,” Transportation Research Part A: Policy and Practice, vol. 77, pp. 167–181, 2015.
  3. S. Teng, X. Hu, P. Deng, B. Li, Y. Li, Y. Ai, D. Yang, L. Li, Z. Xuanyuan, F. Zhu et al., “Motion planning for autonomous driving: The state of the art and future perspectives,” IEEE Transactions on Intelligent Vehicles, 2023.
  4. Y. Hu, K. Li, P. Liang, J. Qian, Z. Yang, H. Zhang, W. Shao, Z. Ding, W. Xu, and Q. Liu, “Imitation with spatial-temporal heatmap: 2nd place solution for nuplan challenge,” arXiv preprint arXiv:2306.15700, 2023.
  5. T. Phan-Minh, F. Howington, T.-S. Chu, S. U. Lee, M. S. Tomov, N. Li, C. Dicle, S. Findler, F. Suarez-Ruiz, R. Beaudoin et al., “Driving in real life with inverse reinforcement learning,” arXiv preprint arXiv:2206.03004, 2022.
  6. O. Scheel, L. Bergamini, M. Wolczyk, B. Osiński, and P. Ondruska, “Urban driver: Learning to drive from real-world demonstrations using policy gradients,” in Conference on Robot Learning.   PMLR, 2022, pp. 718–728.
  7. H. Liu, Z. Huang, and C. Lv, “Occupancy prediction-guided neural planner for autonomous driving,” arXiv preprint arXiv:2305.03303, 2023.
  8. Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, X. Zhu, S. Chai, S. Du, T. Lin, W. Wang et al., “Planning-oriented autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 17 853–17 862.
  9. Z. Huang, H. Liu, J. Wu, and C. Lv, “Conditional predictive behavior planning with inverse reinforcement learning for human-like autonomous driving,” IEEE Transactions on Intelligent Transportation Systems, 2023.
  10. D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta, “Parting with misconceptions about learning-based vehicle motion planning,” arXiv preprint arXiv:2306.07962, 2023.
  11. Y. Chen, P. Karkus, B. Ivanovic, X. Weng, and M. Pavone, “Tree-structured policy planning with learned behavior models,” arXiv preprint arXiv:2301.11902, 2023.
  12. Z. Huang, P. Karkus, B. Ivanovic, Y. Chen, M. Pavone, and C. Lv, “Dtpp: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving,” arXiv preprint arXiv:2310.05885, 2023.
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