Blockchain Enabled by Artificial Intelligence for Self-Driving Cars: Strengthening Consensus Mechanisms, Data Privacy, and Security in Interconnected Vehicle Networks

Abstract

This interdependent network of vehicles has been made possible by the widespread adoption of self-driving cars operating as interconnected swarm networks based on continuous data exchange. Leading a Data-Driven Paradigm Shift However, these networks are not without their challenges, as they are prone to security threats, data privacy vulnerabilities, and inefficient consensus mechanisms to facilitate decentralized decision-making. This study proposes a novel blockchain architecture using AI that could offer enhanced security by utilizing a hybridized consensus protocol for autonomous vehicles. Accordingly, machine learning algorithm-based optimizations in blockchain consensus algorithms, such as PoW, PoS, and DPoS consensus methods, facilitate real-time adaptive adjustments, lower mining costs, and accelerated transaction validation, all within the framework of decentralized trustworthiness. In self-driving car ecosystems, AI-augmented security processes like anomaly detection, deep learning-based intrusion prevention, and federated learning, enhance threat detection while lowering the cybersecurity risks. Moreover, privacy-enhancing cryptographic methods, such as homomorphic encryption, zero-knowledge proofs (ZKPs), and differential privacy, are incorporated to safeguard sensitive vehicle information against unauthorized access while allowing compliance with data privacy laws. Experimental evaluations confirm that the proposed AI-empowered frameworks lead to improved system resilience, optimized resource allocation and improved transaction throughput and latency in contrast to traditional blockchain implementations. Overall, this study demonstrates that using AI-enabled blockchain models can provide a fundamental method for protecting and improving autonomous vehicle networks.