The paper introduces UniV2X, an innovative end-to-end framework for cooperative autonomous driving that integrates key driving modules across diverse views into a unified network. UniV2X addresses the challenges of limited communication bandwidth, latency, and data corruption by proposing a sparse-dense hybrid data transmission and fusion mechanism. This mechanism enhances agent perception, online mapping, and occupancy prediction, ultimately improving planning performance. The framework is evaluated on the DAIR-V2X dataset, demonstrating significant improvements in collision rate and planning performance while maintaining low transmission costs. The paper also includes a detailed analysis of the effectiveness of each fusion module and evaluates the reliability of UniV2X under various communication conditions.The paper introduces UniV2X, an innovative end-to-end framework for cooperative autonomous driving that integrates key driving modules across diverse views into a unified network. UniV2X addresses the challenges of limited communication bandwidth, latency, and data corruption by proposing a sparse-dense hybrid data transmission and fusion mechanism. This mechanism enhances agent perception, online mapping, and occupancy prediction, ultimately improving planning performance. The framework is evaluated on the DAIR-V2X dataset, demonstrating significant improvements in collision rate and planning performance while maintaining low transmission costs. The paper also includes a detailed analysis of the effectiveness of each fusion module and evaluates the reliability of UniV2X under various communication conditions.