  By using various sensors to measure the surroundings and sharing local sensor
information with the surrounding vehicles through wireless networks, connected
and automated vehicles (CAVs) are expected to increase safety, efficiency, and
capacity of our transportation systems. However, the increasing usage of
sensors has also increased the vulnerability of CAVs to sensor faults and
adversarial attacks. Anomalous sensor values resulting from malicious
cyberattacks or faulty sensors may cause severe consequences or even
fatalities. In this paper, we increase the resilience of CAVs to faults and
attacks by using multiple sensors for measuring the same physical variable to
create redundancy. We exploit this redundancy and propose a sensor fusion
algorithm for providing a robust estimate of the correct sensor information
with bounded errors independent of the attack signals, and for attack detection
and isolation. The proposed sensor fusion framework is applicable to a large
class of security-critical Cyber-Physical Systems (CPSs). To minimize the
performance degradation resulting from the usage of estimation for control, we
provide an $H_{\infty}$ controller for CACC-equipped CAVs capable of
stabilizing the closed-loop dynamics of each vehicle in the platoon while
reducing the joint effect of estimation errors and communication channel noise
on the tracking performance and string behavior of the vehicle platoon.
Numerical examples are presented to illustrate the effectiveness of our
methods.

Systems and Control;Systems and Control