Resilient Control Design of the Third-Order Discrete-Time Connected Vehicle Systems Against Cyber-Attacks

Abstract

This paper investigates the consensus problems of the third-order discrete-time Connected Vehicle Systems (CVSs) under cyber-attacks. First, the necessary and sufficient conditions for consensus of third-order discrete-time CVSs are derived in the absence of attacks by using algebraic graph and matrix theory. Then the interaction network framework between the original CVSs in the vehicle platoon layer and a virtual system in the hidden layer is established to resist cyber-attacks. Since sufficiently large attacks can be excluded from CVSs through a threshold defense mechanism, such potential attacks considered are bounded and generated by any linear or non-linear finite L2-gain exogenous dynamics system. It is proved that the stability of CVSs can be ensured and the state errors converge to a bounded range whether the attacks exist only in the vehicle platoon layer or in the overall systems (including the vehicle platoon layer and the hidden layer) by using the Lyapunov stability method. Finally, a simulation example containing several scenarios demonstrates the effectiveness and superiority of the proposed methods.

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

“…Even though self-driving cars will be integrated with complex and effective security mechanisms before hitting the road, they can still be under cyber-attack while operating as the attackers will also explore more complex attacking tools. Considering that, a new wave of research works is emerging, emphasizing on the resilient operation of self-driving cars under cyber-attacks [24]- [32].…”

“…Current research trends are shifting towards achieving this goal of safe operation under cyber-attacks [24], [25] [26]. Vehicle platooning is a great example of such research focus, where the platoon vehicles can detect a malicious member and take decisions without any input from the node under attack [27], [28] [29], [30] [31], [32]. It is important to model the attack parameters or signals, i.e., replay attack, spoofing attack and false data injection attack, within the vehicle operation model so that the attack signals can be isolated and filtered out before using it for decision making.…”