Mamba-KGSC: Knowledge-Guided Semantic Communication for Robust V2V Cooperative Object Detection

Engineering notes

Vehicle-to-Vehicle (V2V) cooperative object detection enhances environmental perception capabilities in complex traffic scenarios by sharing sensory information among vehicles, but limited transmission bandwidth and wireless channel noise can significantly affect the reliable transmission of cross-vehicle semantic features and lead to a degradation in detection performance at the receiver.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Vehicle-to-Vehicle (V2V) cooperative object detection enhances environmental perception capabilities in complex traffic scenarios by sharing sensory information among vehicles, but limited transmission bandwidth and wireless channel noise can significantly affect the reliable transmission of cross-vehicle semantic features and lead to a degradation in detection performance at the receiver. Although existing semantic communication methods based on DeepJSCC can alleviate the cliff effect of traditional separated source–channel coding under low signal-to-noise ratio conditions, they typically rely on additional external autoencoder structures, which increase model complexity and the deployment burden on vehicular edge computing platforms. Meanwhile, under high compression ratios, these methods struggle to adequately preserve detection-related fine-grained information, such as object boundaries, spatial locations, and local structures. Motivated by these challenges, we develop Mamba-KGSC as a lightweight knowledge-guided semantic communication framework for robust V2V cooperative object detection. At the transmitter, Mamba-KGSC utilizes the internal time-scale parameters of the Mamba-YOLO-T backbone network to generate spatial semantic masks, realizing the sparse encoding and transmission of task-relevant features while avoiding the introduction of complex external codec networks. At the receiver, a multi-source knowledge base constraint verification module is constructed to refine the initial detection results by combining physical consistency screening with visual–physical spatial joint redundancy suppression, thereby suppressing physically inconsistent misdetections and repeated detections induced by channel noise. The experimental evaluation indicates that, under a 50% compression ratio, multiple SNR settings, and different channel models, the front-end semantic communication branch of Mamba-KGSC improves mAP@0.5:0.95 by an average of 1.90 percentage points over the DeepJSCC baseline. The multi-source knowledge base constraint verification module further reduces abnormal and duplicate candidate bounding boxes. Overall, Mamba-KGSC provides a balanced solution in terms of transmission cost, detection accuracy, model complexity, and physical consistency, offering a lightweight implementation scheme for robust V2V cooperative detection in challenging communication environments.

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