协同自适应巡航控制系统中的优化控制导向通信
Optimized Control-Centric Communication in Cooperative Adaptive Cruise Control Systems
AI总结：
本研究提出一种基于控制感知通信和基于模型的通信的优化方法，有效减少车辆编队通信开销并保持控制精度，降低通信频率达47%。
摘要：
本研究探讨了一种创新方法，通过车辆到一切（V2X）通信技术来增强车辆编队系统的协同驾驶能力。随着连接与自动驾驶车辆（CAVs）逐渐融入日益密集的交通网络，高效管理通信资源的挑战变得至关重要。我们的重点是优化通信策略，以支持不断增长的互联车辆网络，同时不损害交通安全和效率。我们引入了一个新颖的控制感知通信框架，旨在减少通信开销，同时在车辆编队中维持必要的性能标准。该方法从传统的周期性通信转向更具适应性的非周期性或事件触发方案。此外，我们整合了基于模型的通信（MBC）以在通信条件不理想的情况下增强车辆感知。通过将控制感知通信与MBC相结合，我们的方法有效控制车辆编队，平衡了通信资源节约与控制性能。结果表明，通信频率减少了47%，对控制精度的影响很小，例如速度变化小于1%。广泛的模拟验证了我们结合方法在管理通信和控制方面的有效性，为未来的协同驾驶系统提供了一种有前景的解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
