Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control
Predictive Model-Based and Control-Aware Communication Strategies for Cooperative Adaptive Cruise Control
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DOI：
10.1109/ojits.2023.3259283
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发表时间：
2023
影响因子：
2.6
通讯作者：
Mahdi Razzaghpour;Rodolfo Valiente;Mahdi Zaman;Y. P. Fallah
中科院分区：
文献类型：
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作者：
Mahdi Razzaghpour;Rodolfo Valiente;Mahdi Zaman;Y. P. Fallah
利用车联网（V2X）通信技术，车辆队列驾驶系统有望实现具有更高交通安全和效率水平的合作驾驶新范式。联网和自动驾驶汽车（cav）需要对交通环境有适当的认识。协作排的绩效会受到通信策略的影响。特别地，时间触发或事件触发是我们感兴趣的。随着连接实体数量的增加，与通信相关的费用将显著增加。可以将周期性通信放宽为更灵活的非周期性或事件触发实现，同时保持所需的性能水平。提出了一种基于预测模型和控制感知的车辆排通信解决方案。该方法采用完全分布式的事件触发通信（ETC）策略，结合基于模型的通信（MBC），旨在最大限度地减少通信资源的使用，同时保持理想的闭环性能特征。在我们的方法中，每辆车运行一个基于最近通信模型的远程车辆状态估计器，事件驱动的通信方案仅在性能度量误差超过一定阈值时更新模型。我们的方法显著降低了平均通信速率（82%），同时只略微降低了控制性能（例如，小于1%的速度偏差）。
Utilizing Vehicle-to-everything (V2X) communication technologies, vehicle platooning systems are expected to realize a new paradigm of cooperative driving with higher levels of traffic safety and efficiency. Connected and Autonomous Vehicles (CAVs) need to have proper awareness of the traffic context. The cooperative platoon’s performance will be influenced by the communication strategy. In particular, time-triggered or event-triggered are of interest here. The expenses related to communication will increase significantly as the number of connected entities increases. Periodic communication can be relaxed to more flexible aperiodic or event-triggered implementations while maintaining desired levels of performance. This paper proposes a predictive model-based and control-aware communication solution for vehicle platoons. The method uses a fully distributed Event-Triggered Communication (ETC) strategy combined with Model-Based Communication (MBC) and aims to minimize communication resource usage while preserving desired closed-loop performance characteristics. In our method, each vehicle runs a remote vehicle state estimator based on the most recently communicated model and the event-driven communication scheme only updates the model when the performance metric error exceeds a certain threshold. Our approach achieves a significant reduction in the average communication rate (82%) while only slightly reducing control performance (e.g., less than 1% speed deviation).