发表机构
Tongji University; University of North Carolina at Chapel Hill（同济大学; 北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文从数据、策略和平台三个维度综述端到端自动驾驶训练方法，提出分类框架并展望从数据量到数据价值、从孤立优化到基础模型泛化、从静态训练到训练-测试闭环的未来方向。
AI 中文摘要
自动驾驶是未来智能交通的关键技术，其中端到端学习已成为一种变革性范式，通过统一的可微模型将多模态感官输入直接映射为驾驶动作。尽管具有优势，端到端自动驾驶（E2E-AD）的有效性最终取决于其训练生态系统的质量。本文对E2E-AD的训练方法和生态系统进行了全面综述。我们引入了一种数据-策略-平台（Data-Strategy-Platform）分类法，将训练概念化为一个相互依存的系统。数据层定义了可以学习的内容，策略层控制学习如何与驾驶目标对齐，平台层支持可扩展性和持续演进。在此框架内，我们综述了数据为中心的流水线、学习范式和训练基础设施方面的最新进展，并分析了它们在塑造模型性能、鲁棒性和可部署性方面的相互作用。最后，我们反思了当前的局限性，并提出了前瞻性的愿景，强调从数据数量转向数据价值，从孤立优化转向基于基础模型的泛化，从静态训练转向集成训练-测试循环，旨在实现稳健、可扩展和可信赖的自动驾驶系统。我们维护了一个持续更新的仓库，跟踪前沿文献和作品，网址为\href{this https URL}{Our Project Page}。
英文摘要
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{https://github.com/Jiaaqiliu/Awesome-Training-Ecosystem-for-E2E-AD}{Our Project Page}.
Comments21 pages, 6 figures, accepted by IEEE transactions on intelligent transportation systems
