Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current research mainly focuses on improving individual modules, rather than taking end-to-end learning to optimize final planning performance, resulting in underutilized data potential. In this paper, we introduce UniV2X, a pioneering cooperative autonomous driving framework that seamlessly integrates all key driving modules across diverse views into a unified network. We propose a sparse-dense hybrid data transmission and fusion mechanism for effective vehicle-infrastructure cooperation, offering three advantages: 1) Effective for simultaneously enhancing agent perception, online mapping, and occupancy prediction, ultimately improving planning performance. 2) Transmission-friendly for practical and limited communication conditions. 3) Reliable data fusion with interpretability of this hybrid data. We implement UniV2X, as well as reproducing several benchmark methods, on the challenging DAIR-V2X, the real-world cooperative driving dataset. Experimental results demonstrate the effectiveness of UniV2X in significantly enhancing planning performance, as well as all intermediate output performance. Code is at https://github.com/AIR-THU/UniV2X.

翻译：通过V2X通信协同利用自车与基础设施传感器数据已成为高级自动驾驶领域极具前景的方法。然而，当前研究主要集中于改进单个模块，而非采用端到端学习来优化最终规划性能，导致数据潜力未能充分发挥。本文提出UniV2X——一种开创性的协同自动驾驶框架，该框架将跨视角的所有关键驾驶模块无缝集成至统一网络。我们提出了一种稀疏-稠密混合数据传输与融合机制以实现高效的车辆-基础设施协同，该机制具备三大优势：1）可同时有效增强智能体感知、在线建图及占用预测能力，最终提升规划性能；2）面向实际有限通信条件具有传输友好特性；3）通过混合数据的可解释性实现可靠的数据融合。我们在具有挑战性的真实场景协同驾驶数据集DAIR-V2X上实现了UniV2X，并复现了多种基准方法。实验结果表明，UniV2X在显著提升规划性能的同时，也能改善所有中间模块的输出性能。代码已开源至https://github.com/AIR-THU/UniV2X。