A Testing and Verification Approach to Tune Control Parameters of Cooperative Driving Automation Under False Data Injection Attacks
A Testing and Verification Approach to Tune Control Parameters of Cooperative Driving Automation Under False Data Injection Attacks
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DOI：
10.1109/access.2024.3357357
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发表时间：
2024
期刊：
影响因子：
3.9
通讯作者：
James C. Holland;Farahnaz Javidi-Niroumand;A. J. Alnaser;Arman Sargolzaei
中科院分区：
文献类型：
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作者：
James C. Holland;Farahnaz Javidi-Niroumand;A. J. Alnaser;Arman Sargolzaei
控制系统用于安全关键应用，其中需要调整系统参数以确保安全可靠的运行。调整这些参数的过程可能非常费时且不可靠，因为它们依赖于操作环境。本文讨论了一种用于调整安全协作自适应巡航控制器(CACC)控制参数的测试和验证方法，同时测试算法在虚假数据注入(FDI)攻击下的安全性。在我们的方法中，我们使用粒子群优化(PSO)来整定控制器和观测器的参数。控制器的性能将在控制和检测参数优化前后进行评估。在使用了几个群体后，注意到平均在74次迭代内就能到达全局最优解。总而言之，每个蜂群发现的配置确保了在整个测试过程中实现了安全的跟随距离。然而，就外国直接投资估计而言，最优参数值最小的较为保守的结构表现最好。
Control systems are used in safety-critical applications where tuning the system parameters is required to ensure safe and secure operation. The process of tuning these parameters can be arduous, time-consuming, and unreliable as they are dependent on the operating environment. In this paper, we discuss a testing and verification approach for tuning the control parameters of a secure cooperative adaptive cruise controller (CACC) while simultaneously testing the safety of the algorithm under false data injection (FDI) attacks. In our approach, we use particle swarm optimization (PSO) to tune the parameters of the controller and observer. The performance of the controller will be evaluated before and after the optimization of control and detection parameters. After employing several swarms, it was noticed that the global optimal solution is reached within 74 iterations, on average. In summary, the configurations found by each swarm ensured that a safe following distance was achieved throughout testing. In terms of FDI estimation, however, the more conservative configuration with the minimum optimal parameter values performed the best.