  Cooperative Adaptive Cruise Control (CACC) is a vehicular technology that
allows groups of vehicles on the highway to form in closely-coupled automated
platoons to increase highway capacity and safety, and decrease fuel consumption
and CO2 emissions. The underlying mechanism behind CACC is the use of
Vehicle-to-Vehicle (V2V) wireless communication networks to transmit
acceleration commands to adjacent vehicles in the platoon. However, the use of
V2V networks leads to increased vulnerabilities against faults and cyberattacks
at the communication channels. Communication networks serve as new access
points for malicious agents trying to deteriorate the platooning performance or
even cause crashes. Here, we address the problem of increasing robustness of
CACC schemes against cyberattacks by the use of multiple V2V networks and a
data fusion algorithm. The idea is to transmit acceleration commands multiple
times through different communication networks (channels) to create redundancy
at the receiver side. We exploit this redundancy to obtain attack-free
estimates of acceleration commands. To accomplish this, we propose a
data-fusion algorithm that takes data from all channels, returns an estimate of
the true acceleration command, and isolates compromised channels. Note,
however, that using estimated data for control introduces uncertainty into the
loop and thus decreases performance. To minimize performance degradation, we
propose a robust $H_{\infty}$ controller that reduces the joint effect of
estimation errors and sensor/channel noise in the platooning performance
(tracking performance and string stability). We present simulation results to
illustrate the performance of our approach.
