Designing and Testing A Secure Cooperative Adaptive Cruise Control under False Data Injection Attack

Cooperative adaptive cruise control (CACC) is one of the many advanced driver assistance systems (ADAS) that utilize communication between nearby vehicles to maintain speed while maintaining safe following distances between vehicles. Current CACC algorithms are designed assuming that the communication channel is secure; however, using communication channels make CACC susceptible to adversarial injection attacks, such as False data injections (FDI). This paper validates a developed, novel secure controller which can detect and estimate FDI attacks in real-time. Experimental results show that the designed controller and state estimation techniques ensure accurate tracking under FDI attacks. The effectiveness of the developed controller and detection algorithm is shown in a simulation and tested further on a golf-cart-based vehicle-in-the-loop (ViL) platform.