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A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform — arXiv:2610.00926

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.00926
License (as archived): CC0-1.0
Archived: 2026-10-06T19:16:37.762Z
SHA-256 of the archived text: e9dda87f414b8e8f0aa7d2e1a50f8090b16bbc3b58ca63d2eb59945eff96f070
Stable URL of this page: https://metraipolis.com/documento/dc_debead88593a

How to cite

A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform — arXiv:2610.00926 — arXiv (en), https://arxiv.org/abs/2610.00926 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_debead88593a (Metraipolis archive dc_debead88593a, sha256 e9dda87f414b8e8f0aa7d2e1a50f8090b16bbc3b58ca63d2eb59945eff96f070)

BibTeX

@misc{dcdebead88593a,
  title = {A Survey on End-to-End Autonomous Driving Training from the Perspectives of Data, Strategy, and Platform — arXiv:2610.00926},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.00926},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_debead88593a; sha256 e9dda87f414b8e8f0aa7d2e1a50f8090b16bbc3b58ca63d2eb59945eff96f070},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.00926. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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}.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-10-02 in cs.CV).
Categorie arXiv: Robotics (cs.RO) ; Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.00926 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-06T17:37:36.939Z dalla pagina https://arxiv.org/catchup/cs.CV/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).