Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization

Area of Science:

• Intelligent Transportation Systems (ITS)
• Cybersecurity
• Data Fusion

Background:

• Automated vehicles rely on information fusion from diverse sources for decision-making.
• Current vehicle-to-everything (V2X) security frameworks are inadequate for the complex needs of Mutual Intelligent Transportation Systems (MITS).

Purpose of the Study:

• To develop a robust data fusion security infrastructure for V2X heterogeneous networks.
• To address the limitations of existing security frameworks in MITS.

Main Methods:

• Development of a data fusion security infrastructure with adjustable trust levels.
• Implementation of an area-based Public Key Infrastructure (PKI) architecture accelerated by Graphics Processing Units (GPUs).
• Utilizing artificial neural synchronization for rapid group key exchange.

Main Results:

• The proposed mechanism provides efficient and effective security for multi-source, multi-type data sharing in V2X networks.
• Parametric testing confirmed the solution meets stringent V2X delay requirements.
• The method demonstrated superior efficiency compared to existing strategies.

Conclusions:

• The developed data fusion security infrastructure enhances the security and efficiency of V2X communications.
• The solution is scalable and adaptable to the evolving demands of intelligent transportation.