In this study, we explore an innovative approach to enhance cooperative driving in vehicle platooning systems through the use of vehicle-to-everything (V2X) communication technologies. As Connected and Autonomous Vehicles (CAVs) integrate into increasingly dense traffic networks, the challenge of efficiently managing communication resources becomes crucial. Our focus is on optimizing communication strategies to support the growing network of interconnected vehicles without compromising traffic safety and efficiency. We introduce a novel control-aware communication framework designed to reduce communication overhead while maintaining essential performance standards in vehicle platoons. This method pivots from traditional periodic communication to more adaptable aperiodic or event-triggered schemes. Additionally, we integrate Model-Based Communication (MBC) to enhance vehicle perception under suboptimal communication conditions. By merging control-aware communication with MBC, our approach effectively controls vehicle platoons, striking a balance between communication resource conservation and control performance. The results show a marked decrease in communication frequency by 47\%, with minimal impact on control accuracy, such as less than 1\% variation in speed. Extensive simulations validate the effectiveness of our combined approach in managing communication and control in vehicle platoons, offering a promising solution for future cooperative driving systems.

翻译：摘要：本研究探索了一种创新方法，通过车联万物（V2X）通信技术增强车辆编队系统的协同驾驶能力。随着网联自动驾驶车辆（CAVs）融入日益密集的交通网络，高效管理通信资源的关键挑战愈发凸显。我们聚焦于优化通信策略以支持不断扩大的互联车辆网络，同时不损害交通安全与效率。本文提出一种新型控制感知通信框架，旨在降低通信开销的同时维持车辆编队的关键性能标准。该方法从传统周期性通信转向适应性更强的非周期或事件触发方案。此外，我们集成基于模型的通信（MBC）以在非理想通信条件下增强车辆感知能力。通过将控制感知通信与MBC相结合，本方法有效控制车辆编队，在通信资源节省与控制性能之间取得平衡。研究结果显示，通信频率显著降低47%，而对控制精度影响极小（例如速度变化低于1%）。大量仿真实验验证了所提混合方法在管理车辆编队通信与控制方面的有效性，为未来协同驾驶系统提供了有前景的解决方案。