Resilient Interval Observer-Based Control for Cooperative Adaptive Cruise Control under FDI Attack

TL;DR

This paper presents a resilient control framework for cooperative adaptive cruise control that combines interval observers and neural network estimators to detect and mitigate FDI cyberattacks, ensuring safety and robustness.

Contribution

It introduces a novel integration of interval observer-based estimation with neural network-based attack detection for enhanced resilience in CACC systems.

Findings

Effective FDI attack estimation demonstrated in simulations

Robustness to measurement noise confirmed through MATLAB/Simulink results

Maintains safe inter-vehicle spacing under cyberattack conditions

Abstract

Connectivity in connected and autonomous vehicles (CAVs) introduces vulnerability to cyber threats such as false data injection (FDI) attacks, which can compromise system reliability and safety. To ensure resilience, this paper proposes a control framework combining a nonlinear controller with an interval observer for robust state estimation under measurement noise. The observer bounds leader's states, while a neural network-based estimator estimates the unknown FDI attacks in real time. These estimates are then used to mitigate FDI attack effects maintaining safe inter-vehicle spacing. The proposed approach leverages an idea of interval observer-based estimation and merges model-based and learning-based methods to achieve accurate estimations and real-time performance. MATLAB/Simulink results confirm resilient tracking, precise FDI attack estimation, and robustness to noise, demonstrating potential for real-world CACC applications under cyberattacks, disturbance, and bounded measurement noise.