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Reinforcement learning-based control for autonomous vehicle trajectory tracking under coupled physical-cyber disturbances

Xiaoping Zhao, Jia Guo, Wenbin Huang
September 16, 2026
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Research Abstract & Technology Focus

Abstract This paper presents a reinforcement learning (RL)-based trajectory tracking control method for an autonomous vehicle under coupled road disturbances and denial-of-service (DoS) attacks. Unlike existing studies that treat physical-layer disturbances and network-layer attacks separately, we construct a unified augmented system integrating a seven-degree-of-freedom (7-DOF) vehicle model with a stochastic DoS process. A task-oriented value function is designed by embedding DoS attack probability into the performance index, enabling the controller to adapt its strategy based on attack intensity. To improve learning efficiency, we propose a structured Q-learning approach that updates the optimal control and disturbance strategy online without requiring exact prior knowledge of the vehicle's physical parameters. Simulation results demonstrate that the proposed method outperforms robust control (RC) and adaptive model predictive control (MPC) in tracking accuracy under varying attack intensities, validating the effectiveness of the unified RL-based framework for autonomous vehicles under coupled physical-cyber threats.
Computer science Reinforcement learning Trajectory Controller (irrigation) Embedding Tracking (education)
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What is the core focus of the research titled 'Reinforcement learning-based control for autonomous vehicle trajectory tracking under coupled physical-cyber disturbances'?

This literature focuses on: Abstract This paper presents a reinforcement learning (RL)-based trajectory tracking control method for an autonomous vehicle under coupled road disturbances and denial-of-service (DoS) attacks. Unlike existing studies that treat physical-layer di...

Are there open-source GitHub repositories related to Reinforcement learning-based control for autonomous vehicle trajectory tracking under coupled physical-cyber disturbances?

Yes, open-source projects like Tencent-Hunyuan/UniRL (UniRL is a Framework for Unified Multimodal Model Reinforcement Learning) are actively building upon these concepts.

What other academic literature is closely related to 'Reinforcement learning-based control for autonomous vehicle trajectory tracking under coupled physical-cyber disturbances'?

Yes, highly correlated activity was mapped. An entry titled 'Adaptive Fixed-Time Sliding Mode Tracking Control of Underactuated AUVs with Flexible Prescribed Performance' discusses this: This paper proposes a fixed-time nonsingular terminal sliding mode control (FXTNTSMC) method for underactuated autonomous underwater vehicles (AUVs...

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