V2X Fusion Communication Framework Based on VANETS Collaborative Autonomous Driving

The advancement of collaborative autonomous driving relies on robust and efficient data exchange between vehicles and surrounding infrastructure. Vehicle-to-everything (V2X) fusion communication frameworks, built upon vehicular ad hoc networks (VANETs), enable the integration of heterogeneous data sources to enhance environmental perception and decision-making. However, practical implementation faces significant challenges due to communication interruptions inherent in dynamic VANET environments, leading to incomplete cooperative perception and increased safety risks. To address these challenges, this research proposes a V2X fusion communication framework, incorporating communication-interruption-aware cooperative perception, to ensure reliable information exchange for autonomous vehicles operating in collaborative scenarios. The framework leverages historical cooperation information to compensate for missing data caused by communication disruptions. Furthermore, a communication stochastic temporal convolutional networks (STCN) prediction model is introduced to extract critical features under varying network conditions, enhancing predictive accuracy for lost information. The data were collected from an open-source platform, which includes multi-agent sensor data (LiDAR, radar, and camera), global positioning system (GPS), and timestamped V2X messages simulating realistic vehicular traffic and environmental conditions under varying communication qualities. Packet drop rates were emulated to reflect real-world VANET communication inconsistencies. Additionally, knowledge distillation techniques provide targeted supervision to the predictive model, while curriculum learning strategies stabilize the training process under complex VANET scenarios. The results of the experiments prove that the proposed framework enhanced the perception reliability, and collaborative performance, communication reliability, decreased latency, enhanced obstacle detection accuracy, and decreased error results, including MAE (0.11) and MSE (0.12). This VANET communication architecture is a fusion-based framework that provides reliable, efficient, and safe data-driven collaboration within a group of autonomous vehicles.