Knowledge Graph-Based Methods for Automated Driving

by Rajesh Kumar Dhanaraj PhD (Editor), M. Nalini PhD (Editor), Malathy Sathyamoorthy PhD (Editor), Manar Mohaisen PhD (Editor)

Overview

The global race to develop and deploy automated vehicles is still hindered by significant challenges, with the related complexities requiring multidisciplinary research approaches. Knowledge Graph-Based Methods for Automated Driving offers sought-after, specialized know-how for a wide range of readers both in academia and industry on the use of graphs as knowledge representation techniques which, compared to other relational models, provide a number of advantages for data-driven applications like automated driving tasks. The machine learning pipeline presented in this volume incorporates a variety of auxiliary information, including logic rules, ontology-informed workflows, simulation outcomes, differential equations, and human input, with the resulting operational framework being more reliable, secure, efficient as well as sustainable. Case studies and other practical discussions exemplify these methods' promising and exciting prospects for the maturation of scalable solutions with potential to transform transport and logistics worldwide. - Systematically covers knowledge graphs for automated driving processes - Includes real-life case studies, facilitating an understanding of current challenges - Analyzes the impact of various technological aspects related to automation across a range of transport modes, networks, and infrastructures

Product Details

ISBN-13:	9780443300417
Publisher:	Elsevier Science
Publication date:	04/11/2025
Sold by:	Barnes & Noble
Format:	eBook
Pages:	400
File size:	38 MB
Note:	This product may take a few minutes to download.

About the Author

Dr. Rajesh Kumar Dhanaraj is a professor at the Symbiosis International (Deemed University) in Pune, India. His research and publication interests include cyber-physical systems, wireless sensor networks, and cloud computing. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the Computer Science Teacher Association (CSTA) and member of the International Association of Engineers (IAENG). He is an expert advisory panel member of Texas Instruments Inc. (USA), and an associate editor of International Journal of Pervasive Computing and Communications (Emerald Publishing).
Dr. Nalini holds a PhD in Electronics and Communication Engineering from Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya, Kanchipuram, India. Her research and publication interests include Artificial Intelligence, biomedical engineering, wireless sensor networks, and Internet of Things. She holds two patents in India and has received a grant to submit another application through the AICTE Quality Improvement Schemes supported by the Gvt. of India.
Dr. Malathy holds a PhD in Information and Communication Engineering from Anna University, Chennai, India. Her research areas include wireless sensor networks, Internet of Things, and applied machine learning. She is a life member of the Indian Society for Technical Education (ISTE) and the International Association of Engineers (IAENG). She is an active author/editor for Springer, CRC Press, and Elsevier. She is also a reviewer for Wireless Networks (Springer) and on the editorial board at many international conferences.
Dr. Mohaisen received a Master’s degree in Communications and Signal Processing from the University of Nice Sophia Antipolis, Nice, France, in 2005, and a PhD in Communications Engineering from Inha University, Incheon, South Korea, in 2010. From 2001 to 2004, he worked as a Cell Planning Engineer with the Palestinian Telecommunications Company, Nablus, Palestine. From 2010 to 2019, he was a full-time Lecturer and an Assistant Professor with the Dept. of EEC Engineering Korea Tech, Cheonan, South Korea. He is currently an Associate Professor with the Department of Computer Science, Northeastern Illinois University, Chicago, IL, USA. His research interests include wireless communications with a focus on MIMO systems, systems and internet security, AI applications to security, and social network analysis.

Table of Contents

1. Knowledge graph-based methods for automated driving2. An overview of knowledge representation learning based on ER knowledge graph3. Emerging technologies and tools for knowledge gathering in automated driving4. Awareness of safety regulations and standards for automated driving5. Reliability and ethics developments in knowledge graphs for automated driving6. Role of knowledge graph-based methods in human-AI systems for automated driving7. Knowledge-infused learning: A roadmap to autonomous vehicles8. Integrated machine learning architectures for a knowledge graph embeddings (KGEs) approach9. Future trends and directions for knowledge graph embeddings based on visualization methodologies10. A brief study on evaluation metrics for knowledge graph embeddings11. Design, construction, and recent advancements in temporal knowledge graph for automateddriving12. Knowledge graph-based question answering (KG-QA) using natural language processing13. An integrated framework for knowledge graphs based on battery management14. Ontology-based information integration standards for the automotive industry15. Emerging graphical data management methodologies for automated driving16. Knowledge graphs vs collision avoidance systems: Pros and cons17. Autonomous vehicle collision prediction systems: AI in action with knowledge graphs18. Risk assessment based on dynamic behavior for autonomous systems using knowledge graphs19. Case studies on knowledge graphs in automated driving