A Systematic Review of Artificial Intelligence in Autonomous and Sustainable Transportation Systems: Evolution, Current Applications and Future Directions
DOI:
https://doi.org/10.26740/vubeta.v3i3.52572Keywords:
Artificial Intelligence, Sustainable Transport, Autonomous Mobility, Deep Learning, Smart InfrastructureAbstract
The concept of Artificial Intelligence (AI) rapidly redefines the principles of the modern transportation system, allowing transitioning to autonomous, efficient, and sustainable mobility. This paper provides a systematic review of the history, present, and future of AI in autonomous and sustainable transport systems. Both the foundational studies and recent developments in the field were synthesised in a structured and rigorous methodology. The review follows the development of AI in transportation since primitive rule-based and heuristic methods to more modern data-driven paradigms founded on machine learning and deep learning. It reviews the state-of-the-art applications in major areas, such as traffic control and prediction, self-driving and connected cars, smart infrastructure, safety optimisation and energy-conscious mobility systems. The special focus is made on AI integration with electrical power systems, where smart algorithms can improve energy efficiency, optimise the process of charging electric vehicles, and allow smart grid coordination. Moreover, the paper pinpoints ongoing issues such as data quality constraints, model interpretability, cybersecurity risks, infrastructure constraints and regulatory fragmentation especially in developing areas. In turn, the new research directions are presented, including the potential in energy-conscious AI models, federated learning, federated with digital twin technologies, and multi-modal intelligent mobility ecosystems. Its findings position artificial intelligence as a primary enabler of the next-generation transportation systems, which is a way to make transportation safer, more efficient, and environmentally sustainable.
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