  Autonomous connected vehicles (ACVs) rely on intra-vehicle sensors such as
camera and radar as well as inter-vehicle communication to operate effectively.
This reliance on cyber components exposes ACVs to cyber and physical attacks in
which an adversary can manipulate sensor readings and physically take control
of an ACV. In this paper, a comprehensive framework is proposed to thwart cyber
and physical attacks on ACV networks. First, an optimal safe controller for
ACVs is derived to maximize the street traffic flow while minimizing the risk
of accidents by optimizing ACV speed and inter-ACV spacing. It is proven that
the proposed controller is robust to physical attacks which aim at making ACV
systems instable. To improve the cyber-physical security of ACV systems, next,
data injection attack (DIA) detection approaches are proposed to address cyber
attacks on sensors and their physical impact on the ACV system. To
comprehensively design the DIA detection approaches, ACV sensors are
characterized in two subsets based on the availability of a-priori information
about their data. For sensors having a prior information, a DIA detection
approach is proposed and an optimal threshold level is derived for the
difference between the actual and estimated values of sensors data which
enables ACV to stay robust against cyber attacks. For sensors having no prior
information, a novel multi-armed bandit (MAB) algorithm is proposed to enable
ACV to securely control its motion. Simulation results show that the proposed
optimal safe controller outperforms current state of the art controllers by
maximizing the robustness of ACVs to physical attacks. The results also show
that the proposed DIA detection approaches, compared to Kalman filtering, can
improve the security of ACV sensors against cyber attacks and ultimately
improve the physical robustness of an ACV system.
