Vehicular Resilient Control Strategy for a Platoon of Self-Driving Vehicles under DoS Attack

TL;DR

This paper proposes a resilient control strategy for vehicle platoons that maintains stability and consensus during DoS attacks by dynamically reconfiguring the leader-follower structure.

Contribution

It introduces a novel control approach that detects DoS attacks, switches the attacked leader to a follower, and identifies a new leader to sustain platoon stability.

Findings

The proposed method effectively restores platoon stability under DoS attacks.

Simulation results demonstrate the approach's robustness in real-world scenarios.

The control strategy ensures continuous consensus despite cyber threats.

Abstract

In a platoon, multiple autonomous vehicles engage in data exchange to navigate toward their intended destination. Within this network, a designated leader shares its status information with followers based on a predefined communication graph. However, these vehicles are susceptible to disturbances, leading to deviations from their intended routes. Denial-of-service (DoS) attacks, a significant type of cyber threat, can impact the motion of the leader. This paper addresses the destabilizing effects of DoS attacks on platoons and introduces a novel vehicular resilient control strategy to restore stability. Upon detecting and measuring a DoS attack, modeled with a time-varying delay, the proposed method initiates a process to retrieve the attacked leader. Through a newly designed switching system, the attacked leader transitions to a follower role, and a new leader is identified within a restructured platoon configuration, enabling the platoon to maintain consensus. Specifically, in the event of losing the original leader due to a DoS attack, the remaining vehicles do experience destabilization. They adapt their motions as a cohesive network through a distributed resilient controller. The effectiveness of the proposed approach is validated through an illustrative case study, showing its applicability in real-world scenarios.