  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.
