Stanford Database Library of American Journal of Applied Science and Technology

Vol. 5 No. 08 (2025): Volume 05 Issue 08
Articles

AI-Integrated Intelligent Fleet Ecosystems: Predictive Maintenance, Semantic Intelligence, and Autonomous Logistics Transformationa

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  • Dr. Leonhard Weissmann
Dr. Leonhard Weissmann
University of Mannheim, Germany

Published 2025-08-31

Keywords

  • Intelligent fleet management,
  • predictive maintenance,
  • autonomous vehicles,
  • semantic intelligence

How to Cite

Dr. Leonhard Weissmann. (2025). AI-Integrated Intelligent Fleet Ecosystems: Predictive Maintenance, Semantic Intelligence, and Autonomous Logistics Transformationa. Stanford Database Library of American Journal of Applied Science and Technology, 5(08), 117–123. Retrieved from https://oscarpubhouse.com/index.php/sdlajast/article/view/92

Copyright (c) 2025 Dr. Leonhard Weissmann

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

 

The global transportation and logistics sector is undergoing a profound transformation driven by the convergence of artificial intelligence, autonomous vehicle technologies, Internet of Things infrastructures, and data-intensive decision-making paradigms. Intelligent fleet management has emerged as a central domain where these technologies intersect, enabling predictive maintenance, operational optimization, safety enhancement, and sustainability-driven performance improvements. While early fleet management systems relied primarily on rule-based monitoring and retrospective analytics, contemporary approaches increasingly integrate machine learning, semantic reasoning, knowledge graphs, and real-time data fusion to support autonomous and semi-autonomous decision-making. This research article presents a comprehensive, theory-driven and literature-grounded examination of AI-enhanced fleet management ecosystems, with a particular focus on predictive maintenance for autonomous and connected vehicles. Drawing extensively on interdisciplinary scholarship spanning data science, intelligent transportation systems, logistics, semantic web technologies, and human–machine interaction, the study situates predictive maintenance as a foundational capability that reshapes fleet reliability, cost structures, and operational resilience.

Building on recent advances articulated in the literature, including empirical and conceptual insights into AI-enhanced fleet management and predictive maintenance frameworks (Patil & Deshpande, 2025), this article develops an integrative analytical narrative that connects technological architectures with organizational, economic, and socio-technical implications. The analysis traces the historical evolution of fleet management practices, explores the epistemological foundations of predictive analytics, and critically evaluates the role of semantic enrichment and knowledge-driven intelligence in overcoming data heterogeneity and contextual uncertainty. Particular attention is given to the emergence of autonomous fleets, where predictive maintenance is no longer a supplementary function but a core prerequisite for safety, regulatory compliance, and continuous operation.

Methodologically, the study adopts a qualitative, design-oriented synthesis approach, systematically interpreting existing research, industry reports, and conceptual frameworks to derive coherent insights into system architectures, data flows, and decision-support mechanisms. The results section presents an interpretive consolidation of findings, highlighting how AI-driven predictive maintenance enables proactive fault detection, lifecycle optimization, and adaptive fleet orchestration across logistics and last-mile delivery contexts. The discussion extends these findings through deep theoretical reflection, contrasting competing scholarly perspectives, examining limitations related to data governance, algorithmic transparency, and scalability, and outlining future research trajectories that integrate large language models, digital twins, and augmented intelligence interfaces. By offering an expansive, critically engaged analysis, this article contributes to the academic discourse on intelligent transportation systems and provides a robust conceptual foundation for future empirical research and practical implementation in AI-enabled fleet ecosystems (Gartner, 2023; McKinsey & Company, 2022).

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References

  1. Arena, F., & Pau, G. (2019). An overview of vehicular communications. Future Internet, 11, 27.
  2. Patil, A. A., & Deshpande, S. (2025). Real-time encryption and secure communication for sensor data in autonomous systems. Journal of Information Systems Engineering and Management, 10(415), 41–55.
  3. Haidegger, T., Mai, V., Mörch, C., Boesl, D., Jacobs, A., Rao, B. R., Khamis, A., Lach, L., & Vanderborght, B. (2023). Robotics: Enabler and inhibitor of the Sustainable Development Goals. Sustainable Production and Consumption, 43, 422–434.
  4. Abboud, K., Omar, H. A., & Zhuang, W. (2016). Interworking of DSRC and cellular network technologies for V2X communications: A survey. IEEE Transactions on Vehicular Technology, 65, 9457–9470.
  5. McMahan, H. B., Moore, E., Ramage, D., & y Arcas, B. A. (2016). Federated learning of deep networks using model averaging. arXiv preprint arXiv:1602.05629.
  6. Bauwens, J., Ruckebusch, P., Giannoulis, S., Moerman, I., & De Poorter, E. (2020). Over-the-air software updates in the Internet of Things: An overview of key principles. IEEE Communications Magazine, 58(2), 35–41.
  7. Ghosal, A., & Conti, M. (2020). Security issues and challenges in V2X: A survey. Computer Networks, 169, 107093.
  8. Ylianttila, M., Kantola, R., Gurtov, A., Mucchi, L., Oppermann, I., Yan, Z., Röning, J. (2020). 6G white paper: Research challenges for trust, security and privacy. arXiv preprint arXiv:2004.11665.
  9. Posner, J., Tseng, L., Aloqaily, M., & Jararweh, Y. (2021). Federated learning in vehicular networks: Opportunities and solutions. IEEE Network, 35(2), 152–159.
  10. Kim, K., Kim, J. S., Jeong, S., Park, J. H., & Kim, H. K. (2021). Cybersecurity for autonomous vehicles: Review of attacks and defense. Computers & Security, 102150.
  11. Liu, S., et al. (2019). Edge computing for autonomous driving: Opportunities and challenges. Proceedings of the IEEE, 107(8), 1697–1716.
  12. Bourdoux, A., Barreto, A. N., van Liempd, B., de Lima, C., Dardari, D., Belot, D., & Suutala, J. (2020). 6G white paper on localization and sensing. arXiv preprint arXiv:2006.01779.
  13. Houghtaling, M. A., Fiorini, S. R., Fabiano, N., Gonçalves, P. J., Ulgen, O., Haidegger, T., Carbonera, J. L., Olszewska, J. I., Page, B., & Murahwi, Z. (2024). Standardizing an ontology for ethically aligned robotic and autonomous systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54, 1791–1804.
  14. MacHardy, Z., Khan, A., Obana, K., & Iwashina, S. (2018). V2X access technologies: Regulation, research, and remaining challenges. IEEE Communications Surveys & Tutorials, 20, 1858–1877.
  15. ISO. (2011). ISO 26262: Road vehicles—Functional safety. Geneva: International Organization for Standardization.
  16. SAE International. (2018). Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles (J3016).
  17. Bazzi, A., Cecchini, G., Menarini, M., Masini, B. M., & Zanella, A. (2019). Survey and perspectives of vehicular Wi-Fi versus sidelink cellular-V2X in the 5G era. Future Internet, 11, 122.
  18. Takacs, A., Drexler, D. A., Galambos, P., Rudas, I. J., Haidegger, T., & Csizmadia, T. (2020). Automotive safety in the development pipeline of highly automated vehicles. IEEE Systems, Man, and Cybernetics Magazine, 6, 35–40.
  19. Singh, A., et al. (2019). Detailed comparison of communication efficiency of split learning and federated learning. arXiv preprint arXiv:1909.09145.
  20. Wang, J., Shao, Y., Ge, Y., & Yu, R. (2019). A survey of vehicle to everything (V2X) testing. Sensors, 19, 334.