The Proceedings of 2025 International Conference on Artificial Intelligence and Autonomous Transportation
Description
This book reflects the latest research trends, methods, and experimental results in the field of Artificial Intelligence and Autonomous Transportation, which covers abundant state-of-the-art research theories and ideas. As a vital research area that is highly relevant to current developments in a number of technological domains, the topics covered include Autonomous Transportation Systems, Autonomous Transportation Management and Control Technology, Autonomous Transportation Equipment Technology, Vehicular Networking and Information Security, Emerging Technologies and Future Mobility, Intelligent Water Transportation Technology, Cross-Domain Transportation Technology, and so on. The goal of the proceedings is to provide a major interdisciplinary forum for researchers, engineers, academics, and industry professionals to present the most innovative research and development in the field of Artificial Intelligence and Autonomous Transportation. Engineers and researchers from academia, industry, and government also explore an insight view of the solutions that combine ideas from multiple disciplines in this area. This book serves as an excellent reference work for researchers and graduate students working in the areas of rail transportation, electrical engineering, and information technology.
More details
Other editions
Additional editions
Persons
Shufeng Wang, Professor, Member of the Steering Committee for Logistics Major Teaching in Guangdong Undergraduate Colleges and Universities, Executive Director of China Society of Logistics, Vice Chairman of the Guangdong-Hong Kong-Macao Greater Bay Area Logistics and Supply Chain Innovation Alliance. His main research areas are business economics, transportation economics, logistics economics, international trade, etc.
Zhihong Li, professor and deputy Dean at school of Civil and transportation engineering, Beijing University of Civil Engineering and Architecture. His research areas include smart transportation, traffic planning and design, traffic big data and deep learning, as well as pedestrian flow theory and practice.
Mingyang Pei is an associate professor and doctoral supervisor at the School of Civil Engineering and Transportation, South China University of Technology. She focuses her research on the integration of transportation and energy, intelligent transportation systems, and low-altitude traffic systems.
Wenhui Zhang, Vice Dean, Professor and PhD Supervisor at the School of Civil and Transportation Engineering, Northeast Forestry University. His research areas include traffic safety and environment, as well as applications of traffic big data.
Tianli Tang , Associate Professor. His main research focuses on intelligent transport systems, urban public transport, transport big data, and travel behaviour analysis. Particular emphasis is placed on applying cutting-edge methodologies such as data science, artificial intelligence, and machine learning, integrated with complex systems theory and network science, to advance the optimisation and intelligent development of urban transport systems.
Ying Rong, Associate Professor. She focuses her research on the traffic state analysis, traffic safety, driving behavior analysis, and traffic system optimization.
Content
Methods for State Sensing and Early Warning of Operating Conditions.- Research on Workload Balance-oriented Method of Yard Planning for Fully Automated Container Terminal .- Multimodal Trajectory Prediction for Autonomous Driving on Unstructured Roads using Deep Convolutional Network.- Study on driving risk through unsignalized intersection.- An Engineering Evaluation Methodology of Heterogeneous Network for Highway Infrastructure Monitoring Data Transmission Based on Graph Theory.- Adaptive background residual correlation filters for UAV tracking.- Integrated Travel Service Scheme for ComprehensiveTransportation Hub.- The Research and Application of Gridded Tide Data Service.- Research on Model-Free Adaptive Iterative Learning Control of Quadrotor Aerial Vehicle.- A Parameter Adaptive Model Predictive Control for Virtual Coupling.