发表机构
Boston University; Stanford University; The Ohio State University; Cornell University（波士顿大学; 斯坦福大学; 俄亥俄州立大学; 康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶研究数据碎片化与利用不足的问题，提出社区驱动的数据范式，以提升数据发现、重用与集成，推动实现随时随地的自主性。
AI 中文摘要
自动驾驶已经取得了显著进展，近期的人工智能进展使得商业部署成为可能，正在重塑城市交通。然而，该领域距离其普遍的社会承诺——即能够在任何地点、任何时间、为任何人稳健运行的自动驾驶系统——仍相去甚远。我们认为，这一差距不仅仅是建模问题，更是主流数据范式的问题。当前研究严重依赖少数几个具有有限空间和场景覆盖的基准数据集，尽管社区已在近50个国家共同产出了超过600个自动驾驶数据集。然而，这种丰富性并未转化为广泛的研究影响：大多数数据集由于碎片化、可见性有限、协议不兼容以及基准激励机制将注意力集中在少数主导数据集上，而仍然严重未被充分利用。因此，我们主张自动驾驶研究需要一种协作的、社区驱动的数据范式。这种范式将改进多样化数据集的发现、重用、集成和评估；使未充分探索的数据更容易且更有价值地研究；并降低新贡献者的门槛。我们概述了其关键原则，展示了一个早期实现，并呼吁学术界和工业界合作，将碎片化的数据集转变为共享的社区基础设施，以实现随时随地的自主性。
英文摘要
Autonomous driving has made remarkable progress, with recent AI advances enabling commercial deployments that are reshaping urban mobility. Yet the field remains far from its universal social promise: autonomous systems that can operate robustly anywhere, anytime, for anyone. We posit that this gap is not merely a modeling problem, but a problem of the prevailing data paradigm. Current research relies heavily on a few benchmark datasets with limited spatial and scenario coverage, even though the community has collectively produced over 600 autonomous driving datasets across nearly 50 countries. However, this abundance has not translated into broad research impact: most datasets remain significantly underused due to fragmentation, limited visibility, incompatible protocols, and benchmark incentives that concentrate attention on a few dominant datasets. We therefore argue that autonomous driving research requires a collaborative, community-driven data paradigm. Such a paradigm would improve the discovery, reuse, integration, and evaluation of diverse datasets; make underexplored data easier and more rewarding to study; and lower the barrier for new contributors. We outline its key principles, illustrate an early realization, and call for collaboration across academia and industry to transform fragmented datasets into shared community infrastructure for anytime-anywhere autonomy.
CommentsNeurIPS 2026 Position Paper
