在状态与输入约束下通过控制节点布置与规模增强车辆编队安全性
Enhancing Vehicle Platooning Safety via Control Node Placement and Sizing under State and Input Bounds
AI总结：
本文针对车辆编队易受网络攻击的问题，提出两阶段执行器布置与饱和方法，在状态和输入约束下优化关键控制节点及其输入边界，以兼顾安全性与防御效率。
摘要：
采用协同自适应巡航控制的车辆编队能够提高交通效率、降低能耗并增强安全性，但仍易受网络攻击的影响，这些攻击会破坏通信并导致不安全行为。为应对这些风险，本文研究了控制节点布置与输入边界优化，以在不同条件下平衡安全性与防御效率。我们提出了一种两阶段的执行器布置与执行器饱和方法，重点识别在状态与输入约束下运行时能够最大化系统可控性的关键执行器。通过策略性地布置关键执行器并限制其输入边界，我们确保车辆即使在遭受攻击时也能保持安全距离。仿真结果表明，该方法在保持防御效率的同时有效减轻了攻击影响，为车辆编队安全挑战提供了一种鲁棒的解决方案。
英文摘要：
Vehicle platooning with Cooperative Adaptive Cruise Control improves traffic efficiency, reduces energy consumption, and enhances safety but remains vulnerable to cyber-attacks that disrupt communication and cause unsafe actions. To address these risks, this paper investigates control node placement and input bound optimization to balance safety and defense efficiency under various conditions. We propose a two-stage actuator placement and actuator saturation approach, which focuses on identifying key actuators that maximize the system's controllability while operating under state and input constraints. By strategically placing and limiting the input bounds of critical actuators, we ensure that vehicles maintain safe distances even under attack. Simulation results show that our method effectively mitigates the impact of attacks while preserving defense efficiency, offering a robust solution to vehicle platooning safety challenges.
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。
