Buch, Englisch, 543 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 990 g
Volume III
Buch, Englisch, 543 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 990 g
Reihe: Lecture Notes in Electrical Engineering
ISBN: 978-981-963964-9
Verlag: Springer Nature Singapore
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 will also explore an insight view of the solutions that combine ideas from multiple disciplines in this area. The volumes serve as an excellent reference work for researchers and graduate students working in the areas of rail transportation, electrical engineering, and information technology.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Technische Wissenschaften Verkehrstechnik | Transportgewerbe Verkehrstechnologie: Allgemeines
- Technische Wissenschaften Bauingenieurwesen Verkehrsingenieurwesen, Verkehrsplanung
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
Weitere Infos & Material
The Vehicle Object Detection Algorithm Based on Improved YOLOv8.- Using Real Flight Data to Evaluate Flight Crew's Safety Resilience.- A theoretical analysis of train induced structural vibration of using iron cast comparing with concrete sleeper with under sleeper pad——a case study.- Machine Learning for Ship Energy Consumption Predicting Based on Noon Report Data .- Two-Stream Multi-Modal Target Detection Based on Yolov8 Model under Complex Lighting Conditions.- Enhancing Model Transparency: A Comparative Analysis of SHAP and LIME in Explaining Traffic Accident Prediction Models.- A Goint Calibration Method for Multi Lidar and Camera Based on Parameter Observability Self-assessment.- RMP: Multi-agent Joint Motion Prediction Based on Risky-field Theory.- Numerical Simulation and Impact Analysis of EMU Transformer Fire.- Optimizing the Cold Chain Logistics Distribution Path in O2O Mode under Low Carbon Perspective.
