Towards a Unified Blockchain and AI Architecture for IoT-Enabled Autonomous Vehicles: A Machine Learning Approach

Abstract

AVs are coming into the future of transportation in which the utilization of IoT devices which have the capabilities to drive self-driving cars in live time determining all the collection, decision-making and communication with all the other vehicles as well as the infrastructure is extremely essential. However, security, privacy, data integrity and scalability are key variables of the IoT-enabled AV systems. It is decentralized, secure and transparent manner of the Blockchain technology to attend to these problems and there is the requirement of the artificial intelligence (AI) that provides the necessary functionality of the real-time decision-making and optimization. The paper makes the proposal of the integration of Blockchain and AI to reach one architecture to enhance the performance, security and efficiency of the IoTs that have automated vehicles. The specified architecture is suggested to be applied with the support of the Machine Learning (ML) technique to manage data processing and consensus mechanism in the Blockchain so that it was able to comply with the avenues of scalability and real-time aspects of the AVs. In this research a case will be made of the advantages and limitations and the potential application of such an integrated strategy in autonomous vehicle systems like safe data sharing to autonomous decision making to vehicle to vehicle (V2V) communication. Results of experimental research and case studies prove the effectiveness of such combined work to increase the safety and expandability and efficiency of unmanned auto vehicles running.