AI-Enhanced Fleet Management and Predictive Maintenance for Autonomous Vehicles

Аннотация

Managing a fleet of autonomous vehicles (AVs) efficiently is crucial for keeping them running smoothly and safely. In this paper, we present a Fleet Management System (FMS) that uses data analytics and AI to help fleet managers monitor vehicle performance, predict maintenance needs, and optimize operations. The system continuously collects data from various vehicle sensors and processes it to detect issues like low fuel, battery health, or ADAS faults. It also makes safety recommendations, predicts when vehicles need maintenance, and helps decide the best routes for each vehicle. By combining real-time monitoring with AI-driven decision-making, this system improves safety, reduces downtime, and enhances overall fleet efficiency. We explore how this AI-based approach can transform fleet management and provide a solid foundation for future advancements in autonomous vehicle operations. KEYWORDS Autonomous vehicles, fleet management, predictive maintenance, AI-driven decision-making, real-time monitoring, vehicle performance, route optimization, data analytics, ADAS (Advanced Driver Assistance Systems), sensor data, fleet efficiency, autonomous vehicle operations, AI in transportation 1. INTRODUCTION With the growing adoption of autonomous vehicles (AVs), managing a fleet of these vehicles presents unique challenges that require advanced solutions [1,2]. Autonomous vehicles are equipped with numerous sensors and systems that generate vast amounts of data, including information on vehicle speed, fuel levels, battery health, collision warnings, lane-keeping performance, and other crucial metrics. This data is vital for ensuring the vehicles’ safety, efficiency, and overall performance. However, manually analyzing and managing this data at scale can be an overwhelming task for fleet operators, especially as the number of vehicles in a fleet increase. Fleet management is no longer just about maintaining vehicles; it’s about ensuring that the fleet operates in the most efficient, safe, and cost-effective manner [5]. Autonomous vehicles, due to their self-driving capabilities, can potentially reduce human error, improve safety, and enhance operational efficiency, but they also introduce new complexities. One of the major challenges is keeping track of each vehicle’s performance in real time and proactively addressing any issues that may arise. Without automated monitoring and decision-making tools, fleet operators could miss important signs of malfunction or underperformance, leading to increased downtime, costly repairs, and https://www.academicpublishers.org/journals/index.php/ijdsml 229 AMERICAN ACADEMIC PUBLISHER safety risks. This paper presents an advanced Fleet Management System (FMS) that addresses these challenges by leveraging artificial intelligence (AI) and data analytics. The FMS automates the collection and analysis of fleet data, providing fleet managers with real-time insights into the condition of each vehicle. The system predicts maintenance needs, tracks key performance metrics, and even offers recommendations for optimizing vehicle performance and safety. For example, it can suggest that a vehicle with frequent lane-keeping faults needs a software update or recommend that a vehicle with low fuel levels should be refueled before it runs into operational issues [8]. The main goal of this system is to reduce manual intervention, improve decision-making, and enhance fleet operational efficiency. By predicting when maintenance is needed, fleet managers can address issues proactively before they result in costly breakdowns or delays. Additionally, the system uses AI-driven insights to optimize routes, taking into account variables like fuel consumption, traffic, and vehicle condition to ensure that the fleet operates efficiently, reducing both costs and environmental impact. This paper aims to highlight the value of AI and data-driven decision-making in fleet management. By examining the architecture and functionality of this system, we aim to demonstrate how autonomous vehicle fleets can be managed in a more streamlined, efficient, and safe manner. This approach will not only benefit fleet operators by reducing operational costs and improving vehicle uptime but also have wider implications for the transportation and logistics industries, where large fleets are often used [7,9]. The objectives of this study are to: 1. Develop and implement a comprehensive fleet management system that can manage autonomous vehicles in real-time, monitor vehicle health, and predict when maintenance is needed. 2. Examine the role of AI in predictive maintenance and fleet optimization, illustrating how these technologies can significantly improve the reliability and efficiency of autonomous fleets. 3. Provide a practical framework for fleet operators to monitor fleet performance, track key metrics, and make data-driven decisions to reduce downtime and optimize vehicle performance. 4. Investigate the broader implications of