  To enhance the robustness of cooperative driving to cyberattacks, we study a
controller-oriented approach to mitigate the effect of a class of False-Data
Injection (FDI) attacks. By reformulating a given dynamic Cooperative Adaptive
Cruise Control (CACC) scheme (the base controller), we recognize that the base
controller can be represented by a class of new but equivalent controllers
(base controller realizations) that exhibits the same platooning behavior with
varying robustness in the presence of attacks. We propose a prescriptive
synthesis framework where the base controller and the system dynamics are
written in new coordinates via an invertible coordinate transformation on the
controller state. Because the input-output behavior is invariant under
coordinate transformations, the input-output behavior is unaffected (so
controller realizations do not change the system's closed-loop performance).
However, each base controller realization may require a different combination
of sensors. To this end, we obtain the optimal combination of sensors that
minimizes the effect of FDI attacks by solving a Linear Matrix Inequality
(LMI), while quantifying the FDI's attack impact through reachability analysis.
Through simulation studies, we demonstrate that this approach enhances the
robustness of cooperative driving, without relying on a detection scheme and
maintaining all system properties.
