UniMM-V2X: MoE-Enhanced Multi-Level Fusion for End-to-End Cooperative Autonomous Driving

UniMM-V2X represents a significant leap in the field of autonomous driving by addressing the limitations of existing systems that often operate in isolation. The framework's multi-level fusion strategy allows for enhanced cooperation among agents, improving their ability to perceive, predict, and plan collaboratively. By integrating a Mixture-of-Experts architecture, UniMM-V2X dynamically enhances representations, leading to notable performance gains. Experiments conducted on the DAIR-V2X dataset demonstrate its state-of-the-art capabilities, with improvements in perception accuracy by 39.7%, a 7.2% reduction in prediction error, and a 33.2% enhancement in planning performance. These advancements not only highlight the potential of cooperative autonomous driving but also pave the way for safer and more efficient transportation systems in the future.