In bandwidth-limited and time-varying vehicle–road–cloud cooperative autonomous driving scenarios, real-time transmission and joint inference of high-dimensional multimodal perception data are simultaneously constrained by latency, reliability, and energy consumption. To address these challenges, this article proposes a task-oriented multimodal fusion framework named Multi-Agent Dynamic Diffusion Semantic Communication Network (MA-DDSCNet). On the vehicle side, we design a Task-Guided Multi-Modal Semantic Encoder (TG-MMSE) that performs spatio-temporal alignment, complementary memory gating, and differentiable discrete quantization to compress heterogeneous perception streams from cameras, Light Detection and Ranging (LiDAR), and vehicular state into task-weighted discrete token sequences. A hierarchical distillation scheme is further employed to maintain a unified semantic coordinate system across vehicles, Road Side Units (RSUs), and the cloud. On the communication side, a hierarchical controllable diffusion mechanism adaptively adjusts diffusion noise and time steps according to the importance of object detection, trajectory prediction, and motion planning tasks, as well as link-specific bandwidth budgets. A multi-agent deep scheduler enables collaborative utilization of communication resources among the cloud, RSUs, and vehicles, while an iterative joint semantic decoding and consistency calibration algorithm feeds residuals back into a global memory matrix to suppress semantic drift and yield isomorphic semantic representations at all three layers. Furthermore, we construct a learnable uncertainty-driven multi-objective loss function, combined with a gradient projection strategy, to achieve end-to-end joint optimization of detection, prediction, and planning within a single training loop. Simulation results demonstrate that, compared with baseline methods, MA-DDSCNet achieves average gains of 9.6–18.4% in mean Average Precision (mAP), Average Displacement Error (ADE), Final Displacement Error (FDE), Effective Bit Rate (EBR), and planning safety rate, while reducing the 95th-percentile end-to-end latency to 63 ms, indicating that the proposed framework can significantly enhance the overall performance of semantic perception tasks in complex vehicular networks.
Zou et al. (Mon,) studied this question.