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Data-driven modeling and adaptive event-triggered secure control for autonomous vehicles subject to sensor attacks.

Hong-Tao Sun1, Xinyu Xie1, Miao Rong2

  • 1College of Engineering, Qufu Normal University, Rizhao, China.

ISA Transactions
|October 8, 2025
PubMed
Summary

This study introduces a data-driven method for secure control of autonomous vehicles, addressing sensor attacks. The adaptive event-triggered scheme enhances communication efficiency and control performance against cyber threats.

Keywords:
Autonomous vehiclesDynamic mode decompositionEvent-triggered schemeSecure controlSensor attacks

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Area of Science:

  • Control Engineering
  • Cybersecurity
  • Autonomous Systems

Background:

  • Autonomous vehicles face vulnerabilities from sensor attacks, compromising safety and performance.
  • Traditional control methods struggle with the complexities of real-world sensor attacks and data-driven modeling.

Purpose of the Study:

  • To develop a data-driven secure control strategy for autonomous vehicles against sensor attacks.
  • To enhance communication efficiency and control performance using an adaptive event-triggered mechanism.

Main Methods:

  • Dynamic Mode Decomposition (DMD) for data-driven lateral vehicle model identification.
  • Adaptive event-triggered control scheme to optimize communication and performance.
  • Sliding-mode-like control to counteract sensor attacks.
  • Lyapunov theory and Linear Matrix Inequalities (LMIs) for stability analysis.

Main Results:

  • Successful identification of autonomous vehicle dynamics from data using DMD.
  • Development of an adaptive event-triggered scheme that balances communication load and control effectiveness.
  • Demonstration of effective mitigation of sensor attacks through the proposed control strategy.
  • Validation of the control scheme's effectiveness via comparative simulations.

Conclusions:

  • The proposed data-driven approach effectively addresses sensor attacks in autonomous vehicles.
  • DMD simplifies model identification, while the adaptive event-triggered control enhances system efficiency.
  • The secure control scheme offers a robust solution for enhancing the safety and reliability of autonomous driving.