Multimodal Fusion-Based Semantic Transmission for Road Object Detection
ZHU Zengle1, WEI Zhiwei2, ZHANG Rongqing3, YANG Liuqing1
1. Intelligent Transportation Thrust, The Hong Kong University of Science and Technology(Guangzhou), Guangzhou 511455; 2. Shanghai Research Institute for Intelligent Autonomous Systems, Tongji University, Shanghai 201210; 3. School of Software Engineering, Tongji University, Shanghai 201804
Abstract：In extreme scenarios with long-tail effects, collaborative perception involving multiple vehicles and sensors can provide effective sensory information for vehicles. However, the differentiation in heterogeneous data, coupled with bandwidth constraints and diverse data formats, makes it challenging for vehicles to achieve unified and efficient scheduling in processing. To organically integrate multi-sensor information among different vehicles under limited communication bandwidth, a semantic communication framework for multimodal fusion object detection based on Transformer is proposed in this paper. Unlike traditional data transmission solutions, self-attention mechanisms are utilized in the proposed framework to fuse data from different modalities, focusing on exploring the semantic correlation and dependencies among modal data. It helps vehicles transmit information and collaborate under limited communication resources, thereby enhancing their understanding of complex road conditions. The experimental results on Teledyne FLIR Free ADAS Thermal dataset show that the proposed model performs well in multimodal object detection semantic communication tasks with accuracy of object detection significantly improved and transmission costs reduced by half.
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2023, Vol. 36 
