Secure State Estimation for Motion Monitoring of Intelligent Connected Vehicle Systems

Area of Science:

• Intelligent Transportation Systems
• Cybersecurity in Automotive Engineering
• Control Systems

Background:

• Intelligent connected vehicle systems face state estimation challenges due to false data injection attacks in wireless networks.
• Cooperative Adaptive Cruise Control (CACC) systems are crucial for vehicle dynamics but vulnerable to data manipulation.
• Accurate state estimation is vital for the safe and efficient operation of connected vehicles.

Purpose of the Study:

• To propose a novel secure state estimation method for intelligent connected vehicle systems.
• To reconstruct the motion states of vehicles compromised by false data injection attacks.
• To enhance the reliability of remote monitoring in wireless vehicular networks.

Main Methods:

• Utilizing Cooperative Adaptive Cruise Control (CACC) models with Proportion-Differentiation (PD) controllers for longitudinal dynamics.
• Modeling false data injection attacks using the concept of sparseness.
• Applying compressed sensing principles and L1 norm optimization with orthogonal decomposition for state reconstruction.

Main Results:

• The proposed method effectively reconstructs the motion states of connected vehicles.
• Simulation experiments validate the robustness of the secure state estimation against false data injection.
• The technique enables reliable remote monitoring of intelligent connected vehicle systems.

Conclusions:

• The developed secure state estimation method successfully addresses false data injection attacks.
• The approach enhances the integrity of state information for connected vehicles.
• This work contributes to the security and reliability of intelligent transportation systems.