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Knowledge Graph-Based Methods for Automated Driving
- 1st Edition - April 25, 2025
- Editors: Rajesh Kumar Dhanaraj, M. Nalini, Malathy Sathyamoorthy, Manar Mohaisen
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 4 0 - 0
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 3 0 0 4 1 - 7
The global race to develop and deploy automated vehicles is still hindered by significant challenges, with the related complexities requiring multidisciplinary research ap… Read more
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Request a sales quoteThe 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
Postgraduate students, researchers, and academics in automotive engineering; intelligent transport engineering; robotics and mechatronics; (applied) computing and information science; mechanical engineering; electronics and instrumentation engineering. R&D engineering professionals engaged in intelligent and connected vehicles’ innovations (wireless network sensors, situational awareness software, AI-rooted cognitive processes, etc.) and other technologies fueling advancements in ACES/future mobility trends.
1. Knowledge graph-based methods for automated driving
1.1 Overview of knowledge graph
1.2 Challenges
1.3 Issues
1.4 Potential benefits
2. An overview of knowledge representation learning based on ER knowledge graph
2.1 Types of datasets
2.2 Algorithms for construction of knowledge graph
2.3 Applications
3. Emerging technologies and tools for knowledge gathering in automated driving
3.1 Artificial Intelligence
3.2 Machine learning
3.3 IoT-enabled autonomous vehicles
3.4 Deep learning
3.5 Cloud computing
3.6 Scene Understanding
3.7 Object behavior prediction
3.8 Motion planning
4. Awareness of safety regulations and standards for automated driving
4.1 Development of automated driving systems
4.2 Safety framework
4.3 Core elements, potential approaches, and current activities
4.4 Engineering measures
4.5 Core elements of ADS safety performance
4.6 Process measures
4.7 Safety risk minimization in the design, development, and refinement of ADS
5. Reliability and ethics developments in knowledge graphs for automated driving
5.1 Ethical considerations
5.2 Reliability of data
5.3 Responsible implementation of knowledge graphs
5.4 Handling of critical and sensitive features
6. Role of knowledge graph-based methods in human—AI systems for automated driving
6.1 Knowledge graphs
6.2 Types of knowledge graphs
6.3 Role of knowledge graphs in automated driving
7. Knowledge-infused learning: A roadmap to autonomous vehicles
7.1 Integrating heterogeneous information
7.2 Search and update semantically annotated data at scale
7.3 Re-use knowledge resources across datasets
7.4 Logical reasoning
7.5 Derive clear interpretations and explanations
8. Integrated machine learning architectures for a knowledge graph embeddings (KGEs) approach 8.1 Knowledge graph embedding models
8.2 Notation and problem definition
8.3 Triplet fact-based representation learning models
8.4 Description-based representation learning models
8.5 Applications based on KGE
9. Future trends and directions for knowledge graph embeddings based on visualization methodologies
9.1 Existing knowledge graph system for automated driving
9.2 Future trends of knowledge graph for advanced automated driving systems
10. A brief study on evaluation metrics for knowledge graph embeddings
10.1 Evaluation metrics
10.2 Functions 10.3 Classes
10.4 Preliminaries
10.5 Current evaluation protocols
11. Design, construction, and recent advancements in temporal knowledge graph for automated
driving
11.1 What is a temporal knowledge graph?
11.2 What are temporal graph networks ?
11.3 Popular works with temporal graph networks
11.4 Applications of temporal knowledge graphs
12. Knowledge graph-based question answering (KG-QA) using natural language processing
12.1 Capturing the richness of text as a knowledge graph
12.2 Enhancing the NLP technique with knowledge graphs
12.3 Existing approaches
12.4 Future opportunities
13. An integrated framework for knowledge graphs based on battery management
13.1 Knowledge graph-based data integration framework
13.2 The framework in a battery use case
14. Ontology-based information integration standards for the automotive industry
14.1 Ontology-based data integration
14.2 Sample and actuator (SOSA) ontology
14.3 Connected traffic data ontology (CTDO)
15. Emerging graphical data management methodologies for automated driving
15.1 Automated drivingdata chain
15.2 Analyzing AD/ADS from a big data approach
15.3 Data processing flows in the real and digital world
16. Knowledge graphs vs collision avoidance systems: Pros and cons
16.1 Collision avoidance systems
16.2 Obstacle detection systems
16.3 Potential benefits of knowledge graphs in collision avoidance systems
17. Autonomous vehicle collision prediction systems: AI in action with knowledge graphs
17.1 Learning-based collision prediction
17.2 Model-based collision prediction
17.3 Trajectory prediction
18. Risk assessment based on dynamic behavior for autonomous systems using knowledge graphs
18.1 Obstacle detection
18.2 Weather prediction
18.3 Accident prediction and avoidance
18.4 Vehicle door opening
18.5 Obstacle detection in urban areas
19. Case studies on knowledge graphs in automated driving
19.1 Searching of automated driving data
19.2 Driving distractions’ detection using knowledge graphs
19.3 Situation comprehension for automated driving using knowledge graphs
1.1 Overview of knowledge graph
1.2 Challenges
1.3 Issues
1.4 Potential benefits
2. An overview of knowledge representation learning based on ER knowledge graph
2.1 Types of datasets
2.2 Algorithms for construction of knowledge graph
2.3 Applications
3. Emerging technologies and tools for knowledge gathering in automated driving
3.1 Artificial Intelligence
3.2 Machine learning
3.3 IoT-enabled autonomous vehicles
3.4 Deep learning
3.5 Cloud computing
3.6 Scene Understanding
3.7 Object behavior prediction
3.8 Motion planning
4. Awareness of safety regulations and standards for automated driving
4.1 Development of automated driving systems
4.2 Safety framework
4.3 Core elements, potential approaches, and current activities
4.4 Engineering measures
4.5 Core elements of ADS safety performance
4.6 Process measures
4.7 Safety risk minimization in the design, development, and refinement of ADS
5. Reliability and ethics developments in knowledge graphs for automated driving
5.1 Ethical considerations
5.2 Reliability of data
5.3 Responsible implementation of knowledge graphs
5.4 Handling of critical and sensitive features
6. Role of knowledge graph-based methods in human—AI systems for automated driving
6.1 Knowledge graphs
6.2 Types of knowledge graphs
6.3 Role of knowledge graphs in automated driving
7. Knowledge-infused learning: A roadmap to autonomous vehicles
7.1 Integrating heterogeneous information
7.2 Search and update semantically annotated data at scale
7.3 Re-use knowledge resources across datasets
7.4 Logical reasoning
7.5 Derive clear interpretations and explanations
8. Integrated machine learning architectures for a knowledge graph embeddings (KGEs) approach 8.1 Knowledge graph embedding models
8.2 Notation and problem definition
8.3 Triplet fact-based representation learning models
8.4 Description-based representation learning models
8.5 Applications based on KGE
9. Future trends and directions for knowledge graph embeddings based on visualization methodologies
9.1 Existing knowledge graph system for automated driving
9.2 Future trends of knowledge graph for advanced automated driving systems
10. A brief study on evaluation metrics for knowledge graph embeddings
10.1 Evaluation metrics
10.2 Functions 10.3 Classes
10.4 Preliminaries
10.5 Current evaluation protocols
11. Design, construction, and recent advancements in temporal knowledge graph for automated
driving
11.1 What is a temporal knowledge graph?
11.2 What are temporal graph networks ?
11.3 Popular works with temporal graph networks
11.4 Applications of temporal knowledge graphs
12. Knowledge graph-based question answering (KG-QA) using natural language processing
12.1 Capturing the richness of text as a knowledge graph
12.2 Enhancing the NLP technique with knowledge graphs
12.3 Existing approaches
12.4 Future opportunities
13. An integrated framework for knowledge graphs based on battery management
13.1 Knowledge graph-based data integration framework
13.2 The framework in a battery use case
14. Ontology-based information integration standards for the automotive industry
14.1 Ontology-based data integration
14.2 Sample and actuator (SOSA) ontology
14.3 Connected traffic data ontology (CTDO)
15. Emerging graphical data management methodologies for automated driving
15.1 Automated drivingdata chain
15.2 Analyzing AD/ADS from a big data approach
15.3 Data processing flows in the real and digital world
16. Knowledge graphs vs collision avoidance systems: Pros and cons
16.1 Collision avoidance systems
16.2 Obstacle detection systems
16.3 Potential benefits of knowledge graphs in collision avoidance systems
17. Autonomous vehicle collision prediction systems: AI in action with knowledge graphs
17.1 Learning-based collision prediction
17.2 Model-based collision prediction
17.3 Trajectory prediction
18. Risk assessment based on dynamic behavior for autonomous systems using knowledge graphs
18.1 Obstacle detection
18.2 Weather prediction
18.3 Accident prediction and avoidance
18.4 Vehicle door opening
18.5 Obstacle detection in urban areas
19. Case studies on knowledge graphs in automated driving
19.1 Searching of automated driving data
19.2 Driving distractions’ detection using knowledge graphs
19.3 Situation comprehension for automated driving using knowledge graphs
- No. of pages: 400
- Language: English
- Edition: 1
- Published: April 25, 2025
- Imprint: Elsevier
- Paperback ISBN: 9780443300400
- eBook ISBN: 9780443300417
RD
Rajesh Kumar Dhanaraj
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).
Affiliations and expertise
Professor, Symbiosis International (Deemed University), Pune, IndiaMN
M. Nalini
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.
Affiliations and expertise
Professor, Department of Electronics and Instrumentation Engineering, Sri Sairam Engineering College, Chennai, Tamil Nadu, IndiaMS
Malathy Sathyamoorthy
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.
Affiliations and expertise
Assistant Professor, Department of Information Technology, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, IndiaMM
Manar Mohaisen
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.
Affiliations and expertise
Associate Professor, Department of Computer Science, Northeastern Illinois University, Chicago, IL, USA