Information Fusion in Autonomous Vehicle Using Artificial Neural Group Key Synchronization.

Mohammad Zubair Khan,Hamza Ghandorh,Wadii Boulila,Arindam Sarkar,Maha Driss

doi:10.3390/s22041652

Abstract

Information fusion in automated vehicle for various datatypes emanating from many resources is the foundation for making choices in intelligent transportation autonomous cars. To facilitate data sharing, a variety of communication methods have been integrated to build a diverse V2X infrastructure. However, information fusion security frameworks are currently intended for specific application instances, that are insufficient to fulfill the overall requirements of Mutual Intelligent Transportation Systems (MITS). In this work, a data fusion security infrastructure has been developed with varying degrees of trust. Furthermore, in the V2X heterogeneous networks, this paper offers an efficient and effective information fusion security mechanism for multiple sources and multiple type data sharing. An area-based PKI architecture with speed provided by a Graphic Processing Unit (GPU) is given in especially for artificial neural synchronization-based quick group key exchange. A parametric test is performed to ensure that the proposed data fusion trust solution meets the stringent delay requirements of V2X systems. The efficiency of the suggested method is tested, and the results show that it surpasses similar strategies already in use.

Highlights

Mutual Intelligent Transport Systems (MITS) are transportation systems in which two or more ITS sub-systems facilitate and deliver an ITS solution with higher quality and service level than if only one of the ITS sub-systems worked together
In order to successfully execute keys that serve as a foundation for all levels of information trust services in V2X networks, an area-based key sharing technique is created
The key sharing procedure may be viewed as one of the information communication processes that require the maximum level of confidence

Summary

Introduction

Mutual Intelligent Transport Systems (MITS) are transportation systems in which two or more ITS sub-systems (personal, car, roadside, and centralized) facilitate and deliver an ITS solution with higher quality and service level than if only one of the ITS sub-systems worked together. Data amalgamation in MITS has shown to be extremely effective in making the transport systems easier and safer. By minimizing traffic jams, offering smart transport techniques, drastically decreasing the frequency of road accidents, and eventually achieving autonomous vehicles, the data-based technologies implemented in MITS promise to profoundly transform a person’s driving experiences [1,2,3]. Data may be gathered and amalgamated on many MITS modules due to the advancement of software and hardware. At the other side, data blending and analyzing techniques, Artificially Intelligent (AI) techniques, have improved to satisfy the actual data amalgamation requirements of MITS channels

Methods

Results

Conclusion