arXiv：cs.LG（机器学习，全量分类）· Chengkai Xu, Yiming Cui, Jiaqi Liu, Yicheng Guo, Cheng Qin, Geyuan Zhang, Xinwei Dong, Shiyu Fang, Peng Hang, Jian Sun·· 12 小时前AI 评分32
端到端自动驾驶训练综述：从数据、策略与平台视角出发
A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform
AI 导读
一篇被 IEEE Transactions on Intelligent Transportation Systems 接收的综述提出 Data-Strategy-Platform 分类体系，将端到端自动驾驶（E2E-AD）训练视为数据、策略、平台三层相互依赖的系统。
正文
Abstract:Autonomous driving is a cornerstone technology for the future of intelligent transportation, where end-to-end learning has emerged as a transformative paradigm that directly maps multimodal sensory inputs to driving actions through unified differentiable models. While offering advantages, the effectiveness of end-to-end autonomous driving (E2E-AD) is ultimately determined by the quality of its training ecosystem. This paper provides a comprehensive review of training methods and ecosystem for E2E-AD. We introduce a Data-Strategy-Platform taxonomy that conceptualizes training as an interdependent system. The data layer defines what can be learned, the strategy layer governs how learning aligns with driving objectives, and the platform layer supports scalability and continuous evolution. Within this framework, we survey recent advances across data-centric pipelines, learning paradigms, and training infrastructures, and analyze their interplay in shaping model performance, robustness, and deployability. Finally, we reflect on current limitations and articulate a forward-looking vision that emphasizes a shift from data quantity to data value, from isolated optimization to foundation-driven generalization, and from static training to integrated training-testing loops, aiming toward robust, scalable, and trustworthy autonomous driving systems. We maintain a continuously updated repository tracking cutting-edge literature and works at \href{this https URL}{Our Project Page}.
| Comments: | 21 pages, 6 figures, accepted by IEEE transactions on intelligent transportation systems |
| Subjects: | Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.00926 [cs.RO] |
| (or arXiv:2610.00926v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.00926 arXiv-issued DOI via DataCite (pending registration) |
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| Related DOI: | https://doi.org/10.1109/TITS.2026.3695999
DOI(s) linking to related resources |
Submission history
From: Chengkai Xu [view email]
[v1]
Thu, 1 Oct 2026 02:00:52 UTC (4,389 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org