AI-Enhanced Fleet Management and Predictive Maintenance for Autonomous Vehicles

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.

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Publication Details

Journal
International journal of data science and machine learning.
Published
2025-05-24
DOI
https://doi.org/10.55640/ijdsml-05-01-21
Primary Topic
Vehicle emissions and performance
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-Enhanced Fleet Management and Predictive Maintenance for Autonomous Vehicles

Aishwarya Ashok Patil, Spriha Deshpande
International journal of data science and machine learning.
Vehicle emissions and performance
article

AI-Enhanced Fleet Management and Predictive Maintenance for Autonomous Vehicles

Aishwarya Ashok Patil, Spriha Deshpande
article en

Abstract

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.

International journal of data science and machine learning.Vol. 5(1)
San Jose State University (US), Savitribai Phule Pune University (IN)
Openalex Percentile: Top 88%
Vehicle emissions and performance
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