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End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems — arXiv:2610.01746

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.01746
License (as archived): CC0-1.0
Archived: 2026-10-06T19:16:55.488Z
SHA-256 of the archived text: 6ca19aefc601fd41ad86acdbf6d06c0e1e6f0c2de892e884f36c634c55550a04
Stable URL of this page: https://metraipolis.com/documento/dc_01d41bb5ebe2

How to cite

End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems — arXiv:2610.01746 — arXiv (en), https://arxiv.org/abs/2610.01746 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_01d41bb5ebe2 (Metraipolis archive dc_01d41bb5ebe2, sha256 6ca19aefc601fd41ad86acdbf6d06c0e1e6f0c2de892e884f36c634c55550a04)

BibTeX

@misc{dc01d41bb5ebe2,
  title = {End-to-End Learning vs. Modular Architectures: Comparative Insights into Autonomous Driving Systems — arXiv:2610.01746},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.01746},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_01d41bb5ebe2; sha256 6ca19aefc601fd41ad86acdbf6d06c0e1e6f0c2de892e884f36c634c55550a04},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.01746. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Autonomous driving systems have become a central focus of intelligent transportation research, with End-to-End Learning and Modular Architectures offering two prominent design paradigms for their implementation. E2E Learning uses deep learning algorithms to map raw sensory inputs directly to driving actuators, providing a streamlined and adaptable solution. while Modular Architectures employ a pipeline-based approach, dividing the system into distinct subsystems for perception, cognition, planning, and control. This paper presents a comprehensive comparative analysis of these paradigms, focusing on their strengths, limitations, and trade-offs to provide insights into their suitability for various autonomous driving applications. The study evaluates key factors such as interpretability, scalability, robustness, and real-world applicability. While End-to-End Learning emphasizes simplicity and adaptability in dynamic environments, it lacks transparency and is highly dependent on large datasets. Conversely, Modular Architectures offer superior interpretability and task-specific optimization, but face challenges related to integration complexity and scalability. To address these limitations, hybrid approaches that combine the strengths of both paradigms have emerged, offering a promising direction for overcoming these challenges. Beyond this comparative synthesis, following work proposes a Four-Dimensional Architecture Selection Framework, comprising twelve binary criteria across safety, operating environment, data/computational resources, and deployment context, and validate it against ten published autonomous driving systems, correctly recommending 7/10 deployed architectures. This work synthesizes existing literature to highlight key trade-offs between the paradigms and identifies hybrid architectures as a promising direction for future research.

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/2610.01746 — 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 2026-10-06T17:37:36.956Z 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).