Task-oriented V2X Communications using Large Multi-modality Model

In the 5G NR and 6G, vehicle-to-everything (V2X) communication has emerged as a crucial technology. In this paper, we present a novel task-oriented communications (ToC) approach that employs large multi-modal models (LMMs) for V2X tasks such as traffic management. The key idea of the proposed multi-modal task-oriented communications (MMToC) is to extract essential information relevant to vehicular tasks from the various sensing data, integrate the information, and infer the output of vehicular tasks using LMMs. Unlike traditional communication methods focusing on bit-level metrics such as bits per second and error rate, MMToC enhances task efficiency and performance by optimizing information transmission methods for specific vehicular tasks. MMToC exchanges only the essential feature vectors needed for vehicular tasks, thereby reducing communication overhead and improving task performance. Numerical results demonstrate that the proposed MMToC technique significantly increases average vehicle speed by more than 40% compared to conventional methods.