Guest Editors
Dr. Yixuan Geng
Email: geng@hku.hk
Affiliation: Department of Mechanical Engineering, University of Hong Kong, Hong Kong, China
Homepage:
Research Interests: AI-enhanced UAV systems for structural defect detection in transportation infrastructure, 3D point cloud processing and semantic analysis using deep learning, reinforcement learning for collaborative UAV swarm intelligence
Assoc. Prof. Yunpeng Wu
Email: wuyunpeng@kust.edu.cn
Affiliation: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, China
Homepage:
Research Interests: intelligent inspection of transportation infrastructure, structural health monitoring, computer vision, edge computing
Dr. Zhiwei Cao
Email: zhiwei@bjtu.edu.cn
Affiliation: State Key Laboratory of Advanced Rail Autonomous Operation, Beijing Jiaotong University, Beijing, China
Homepage:
Research Interests: intelligent transportation, railway safety, infrastructure disease detection, machine vision
Prof. Fengxiang Guo
Email: guofengxiang@kust.edu.cn
Affiliation: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, China
Homepage:
Research Interests: intelligent transportation health monitoring and safety
assessment, transportation big data mining and analysis
Assoc. Prof. Sihui Long
Email: longsh@kust.edu.cn
Affiliation: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming, China
Homepage:
Research Interests: train operation optimization, coordinated optimization of comprehensive transportation
Summary
With the rapid integration of artificial intelligence with intelligent sensing technologies, intelligence platforms, and connected transportation infrastructure, low-altitude transportation systems are transitioning from passive operations toward embodied intelligence and environment-aware intelligence. As a cutting-edge research frontier in both academia and industry, low-altitude intelligent transportation systems integrate innovations from transportation, aerospace, embodied cognition, and device development, while serving the strategic objectives of enabling autonomous and interactive inspection of transportation infrastructure, supporting intelligent traffic operations, enhancing traffic efficiency, facilitating adaptive logistics, expanding personal mobility options, and reshaping the future urban transportation ecosystem.
Driven by innovations in embodied intelligence, low-altitude transportation systems face the challenge of performing real-time perception, adaptive decision-making, and responsive control under dynamic traffic conditions and environmental disturbances. Advanced artificial intelligence algorithms (including deep learning, reinforcement learning, and multi-agent optimization) integrated with multi-source sensing and perception data (such as visual and infrared imagery, LiDAR point clouds, inertial measurements, and environmental signals) demonstrate substantial potential for structural health monitoring, autonomous interactive infrastructure inspection, environment-aware path planning, adaptive traffic management, predictive maintenance, and traffic safety assessment. The development and deployment of these technologies can effectively address the complex challenges of low-altitude traffic operations and promote the evolution of transportation systems toward a more autonomous, resilient, and sustainable paradigm of embodied intelligence.
Topics of interest include, but are not limited to:
· Low-altitude structural health monitoring systems
· UAV-enabled autonomous and non-destructive measurement of traffic infrastructure
· Multi-source sensing and data fusion for transportation environment awareness
· Traffic safety assessment and risk mitigation supported by environment-aware perception
· Low-altitude drone intelligent perception systems
· Autonomous infrastructure inspection technology in GPS-denied environments
· Anti-disturbance adaptive control of UAVs
· Planning and optimization of transportation operations based on environmental perception
Keywords
structural health monitoring (SHM), low-altitude economy, safety evaluation, artificial intelligence (AI), drone-based inspection, computer vision
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