Cooperative Multi-Modal Semantic Communication Scheme for Semantic Segmentation in Autonomous Driving Systems

In recent years, multi-modal semantic segmentation in autonomous driving has garnered significant attention due to its effectiveness under challenging lighting conditions. However, current segmentation approaches primarily focus on segmentation techniques without addressing the critical communication challenges inherent in internet of vehicles (IoV). Unlike traditional communications that transmit source data, semantic communications transmit only task-relevant semantic information, significantly reducing data traffic while ensuring the accuracy of task execution. This paper introduces a novel cooperative multi-modal semantic communication framework designed to enhance semantic segmentation in autonomous driving systems. By compressing redundant information and transmitting only essential semantic features, the proposed scheme enables continuous data transmission with drastically reduced data volume. Moreover, this scheme not only improves communication efficiency but also ensures reliable segmentation performance across diverse data modalities. Experimental results validate the effectiveness of proposed scheme, demonstrating its ability to achieve high compression ratios, robust segmentation performance, and re-silience to channel noise under varying lighting conditions.