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.

Autonomous vehicles, fleet management, predictive maintenance, AI-driven decision-making, real-time monitoring, vehicle performance, route optimization, data analytics

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