Problem
Research questions and friction points this paper is trying to address.
End-to-End Autonomous Driving
Training Ecosystem
Data-Centric
Learning Strategy
Training Platform
A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform
🤖 AI Summary
This study addresses the absence of a systematic review of the end-to-end autonomous driving training ecosystem by proposing a three-tier taxonomy encompassing data, strategy, and platform. It comprehensively surveys existing training methodologies and their interaction mechanisms through an interdependent system architecture that integrates data-centric pipelines, diverse learning paradigms, and scalable infrastructure. Furthermore, this work articulates a new vision for transitioning from data-scale reliance to value-driven evolution, advancing foundation model generalization, and establishing closed-loop integration testing. To support ongoing research, the project maintains a continuously updated literature repository, delineating promising research trajectories toward enhancing model robustness, scalability, and trustworthiness in autonomous driving systems.
📝 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{https://github.com/Jiaaqiliu/Awesome-Training-Ecosystem-for-E2E-AD}{Our Project Page}.
Innovation
Methods, ideas, or system contributions that make the work stand out.
End-to-End Autonomous Driving
Data-Strategy-Platform Taxonomy
Training Ecosystem
Foundation-driven Generalization
Training-Testing Loops
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