Enhancing Cyber Resilience of Autonomous Vehicles Against Sensor and Actuator Attacks using Cascaded Secure State Estimations

Amidst the ongoing technological advancements, it is imperative to address the growing concern of cyber attacks targeting actuators and sensors in autonomous vehicles. This paper presents a novel approach that involves the cascading of two distinct secure state estimation algorithms. These algorithms employ separate nonlinear observers, each dedicated to estimating either sensor attacks or actuator attacks. We derive the necessary conditions to ensure the effectiveness of this dual-observer setup. Additionally, we incorporate a Lyapunov-based composite control law to counteract the impact of these attacks on the system. Through simulations focused on the point stabilization of autonomous heavy-duty vehicles, we illustrate the efficacy of our approach. Our results validate the convergence and robustness of our proposed estimation method and demonstrate the stability of the designed control system. These findings highlight the potential for real-world implementation, offering enhanced operational safety for systems.