  Connected and Automated Vehicles (CAVs) rely on the correctness of position
and other vehicle kinematics information to fulfill various driving tasks such
as vehicle following, lane change, and collision avoidance. However, a
malicious vehicle may send false sensor information to the other vehicles
intentionally or unintentionally, which may cause traffic inconvenience or loss
of human lives. Here, we take the advantage of cloud-computing and increase the
resilience of CAVs to malicious vehicles by assuming each vehicle shares its
local sensor information with other vehicles to create information redundancy
on the cloud side. We exploit this redundancy and propose a sensor fusion
algorithm for the cloud, capable of providing a robust state estimation of all
vehicles in the cloud under the condition that the number of malicious
information is sufficiently small. Using the proposed estimator, we provide an
algorithm for isolating malicious vehicles. We use numerical examples to
illustrate the effectiveness of our methods.
