Resilient Supervisory Control of Autonomous Intersections in the Presence of Sensor Attacks

TL;DR

This paper develops a resilient supervisory control framework for autonomous vehicle intersections that maintains safety and avoids deadlock even under stealthy sensor attacks, enhancing CPS security.

Contribution

It introduces a novel resilient supervisory control system with an integrated detector to handle undetectable sensor attacks in autonomous intersections.

Findings

The system remains safe under stealthy sensor attacks.

It prevents deadlock and ensures maximal permissiveness.

Demonstrated effectiveness through illustrative examples.

Abstract

Cyber-physical systems (CPS), such as autonomous vehicles crossing an intersection, are vulnerable to cyber-attacks and their safety-critical nature makes them a target for malicious adversaries. This paper studies the problem of supervisory control of autonomous intersections in the presence of sensor attacks. Sensor attacks are performed when an adversary gains access to the transmission channel and corrupts the measurements before they are received by the decision-making unit. We show that the supervisory control system is vulnerable to sensor attacks that can cause collision or deadlock among vehicles. To improve the system resilience, we introduce a detector in the control architecture and focus on stealthy attacks that cannot be detected but are capable of compromising safety. We then present a resilient supervisory control system that is safe, non-deadlocking, and maximally permissive, despite the presence of disturbances, uncontrolled vehicles, and sensor attacks. Finally, we demonstrate how the resilient supervisor works by considering illustrative examples.