An Intelligent AI‐Based Fault Detection Mechanism for Autonomous Vehicles with Blockchain Security

The evolution of automation has witnessed a remarkable transformation from traditional rule-based systems to intelligent automation, driven by the convergence of artificial intelligence, machine learning, and robotic process automation. This paradigm shift has propelled automation capabilities beyond mere task execution to encompass contextual awareness, decision-making, and continuous learning. The exponential growth in computing power has enabled the processing and analysis of vast amounts of data, a crucial prerequisite for intelligent automation. The integration of artificial intelligence into industrial control systems has revolutionized the manufacturing industry and the automobile industry, marking a significant leap forward in the fourth industrial revolution. AI-powered autonomous systems have the following key features: a) Autonomous decision-making; b) Enhanced Adaptability and Flexibility c) Improved Efficiency and Reduced Human Errors; c) Optimized Performance d) Early anomaly detection; and e) ensuring product quality. Autonomous vehicles, also known as self-driving cars, are vehicles that can operate without human input. They are equipped with sensors, software, and actuators that allow them to perceive their environment, make decisions, and control their movements. Autonomous vehicles have the potential to revolutionize transportation by making it safer, more efficient, and more accessible. Technological advancements in autonomous vehicles have led to the increased complexity and intelligence of electrical and mechanical components. This complexity raises the likelihood of faults, which can have severe consequences if not detected early. Early fault detection is essential for preventing accidents and minimizing economic losses. Along with sensors, actuators, processors, and complex software algorithms, autonomous engines also can cause faults. In addition to the mechanisms and solutions proposed for fault detection concerning sensor failure, we also need to address autonomous engine faults. We also need to mitigate the possible malicious attacks to secure the data. To achieve these goals, an AI-based fault detection scheme with blockchain security is designed for autonomous vehicles. The ML model applied can detect autonomous vehicle engine faults accurately, and the decentralized, immutable blockchain can secure the entire framework. Fault detection and fault-tolerance control are crucial aspects of industrial control systems, ensuring system stability and dependability. Fault detection identifies anomalies or malfunctions in the control system and its associated equipment, enabling timely intervention to prevent disruptions. Fault tolerance control, on the other hand, allows control systems to continue functioning effectively even in the presence of faults or malfunctions, often employing adaptive control strategies or redundancy mechanisms. These combined efforts enhance system reliability, minimize downtime, and optimize maintenance costs. Although various fault detection methods have been proposed for autonomous vehicles, they often overlook engine faults, a major cause of road accidents. Additionally, security vulnerabilities during autonomous vehicle communication can expose their data to unauthorized access, potentially compromising system performance. To address these challenges, we propose an AI-based engine fault detection scheme for autonomous vehicles that utilizes blockchain technology. The combination of blockchain and machine learning ensures secure and accurate engine fault prediction, thereby reducing the likelihood of road accidents.