A Testing and Verification Approach to Tune Control Parameters of Cooperative Driving Automation Under False Data Injection Attacks

Abstract

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.

Keywords

• Cooperative driving automation
• false data injection attack
• Lyapunov stability
• parameter tuning
• particle swarm optimization
• testing