Secure Control Design for Cooperative Adaptive Cruise Control Under False Data Injection Attack

Abstract

Cooperative adaptive cruise control (CACC) is one of the many advanced driver assistance systems (ADAS) that leverage communication between nearby vehicles to maintain speed while ensuring safe following distances. The current CACC algorithms are designed with the assumption that the communication channel is secure. However, the use of communication channels makes CACC susceptible to attacks, such as False data injection (FDI). This paper develops a novel secure nonlinear controller and a nonlinear observer which can estimate FDI attacks in real-time. Furthermore, this paper shows that the developed controller and FDI attack estimation techniques ensure semi-globally uniformly bounded tracking under FDI attacks, noise, and disturbances. The efficaciousness of the proposed CACC algorithm was demonstrated in simulation and through experimental implementation. During testing using both methodologies, the controller was able to maintain a safe following distance and estimate FDI attacks and noise in real-time.