AI-Powered Smart Transportation: From Predictive Maintenance to Autonomous Control
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 11
Special Issue Editor
Interests: intelligent transportation system; electric vehicles
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid development of artificial intelligence is reshaping smart transportation by enabling both predictive maintenance and autonomous control in a unified framework. Predictive maintenance techniques, driven by prognostics and health management (PHM), allow for the real-time monitoring of critical assets such as batteries, drivetrains, and infrastructure, providing accurate remaining useful life estimation and anomaly detection. These methods improve system reliability, reduce downtime, and lower operational costs. At the same time, autonomous driving and control systems leverage trajectory prediction and motion planning to achieve safe, efficient, and human-like driving behaviors. With the support of cooperative perception and vehicle-to-everything (V2X) communication, vehicles can share intent, extend their sensing range, and operate collaboratively within dynamic traffic environments.
Recent progress in edge AI and embedded machine learning makes it possible to deploy intelligent decision-making directly in vehicles and roadside units, ensuring low-latency responses while maintaining scalability. Reinforcement learning and digital twin technologies further contribute to network-level traffic management, energy optimization, and adaptive control strategies. Safety verification and validation remain a crucial research frontier, addressing the need for assessments of the uncertainty, robustness, and trustworthiness of AI-powered systems. In addition, federated learning and privacy-preserving approaches are essential to balance data utility with security and compliance requirements.
By bringing together predictive maintenance and autonomous control, this Special Issue aims to provide a platform for innovative solutions that ensure reliability, safety, and scalability of next-generation transportation systems.
Dr. Zhiheng Li
Guest Editor
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Keywords
- predictive maintenance
- prognostics and health management (PHM)
- autonomous driving and control
- trajectory prediction and motion planning
- cooperative perception and V2X
- edge AI and embedded ML
- reinforcement learning for traffic management
- digital twins for transportation
- safety verification and validation
- federated learning and privacy
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