自动驾驶研究需要社区驱动的数据范式
原标题：Autonomous Driving Research Requires a Community-Driven Data Paradigm
AI 摘要
该论文指出自动驾驶研究过度依赖KITTI、nuScenes、Waymo Open Dataset等少数基准数据集，尽管社区已在近50个国家发布了600多个真实世界数据集，但多数因碎片化、可见性低、协议不兼容和基准激励而未被充分利用。作者主张采用社区驱动的数据范式，以改善数据发现、复用、整合与评估，降低新贡献者门槛，并呼吁产学研合作将碎片化数据集转化为共享社区基础设施。
正文节选
Autonomous Driving Research Requires a Community-Driven Data Paradigm Abstract 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