Intelligent Cooperative Perception Technology for Vehicles and Experiments Based on V2V/V2I Semantic Communication

In recent years, intelligent driving has attracted more and more attention due to its potential to revolutionize transportation safety and efficiency, emerging as a disruptive technology that reshapes the future landscape of transportation. Environmental perception serves as the primary and fundamental cornerstone of intelligent driving systems. To address the intrinsic blind spots in environmental perception, this paper presents a vehicle collaborative perception approach based on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) semantic communication. Specifically, a Transformer-based semantic segmentation technique is proposed for application to images acquired from surrounding vehicles and ground-based cameras. Subsequently, the generated semantic segmentation maps are transmitted via V2V/V2I communication. In the receiver, a semantic-guided image reconstruction technique based on Generative Adversarial Networks (GANs) is developed to generate images with high realism. The generated Image images can be further fused with locally perceived data, facilitating intelligent collaborative perception. This method achieves effective elimination of blind spots. Furthermore, as only semantic segmentation maps—with a data size significantly smaller than that of raw images—are transmitted instead of the latter, it exhibits excellent adaptability to the dynamically time-varying characteristics of V2V/V2I channels. Even in poor channel condition, the proposed method maintains high reliability and real-time performance.