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.
1. Knowledge Graph-Based Methods for Automated Driving - p. i 2. Cover image - p. i 3. Title page - p. i 4. Table of Contents - p. i 5. Copyright - p. iv 6. Contributors - p. xiii 7. 1 Knowledge graph-based method for self-driving cars in Industry 5.0 - p. 1 8. 2 An overview of knowledge representation learning based on ER knowledge graph - p. 19 9. 3 Emerging technologies and tools for knowledge gathering in automated driving - p. 35 10. 4 Safety regulations and standards for automated driving - p. 59 11. 5 Reliability and ethics developments in knowledge graphs for automated driving - p. 79 12. 6 Role of knowledge graph-based methods in human—AI systems for automated driving - p. 99 13. 7 Knowledge-infused learning: A roadmap to autonomous vehicles - p. 119 14. 8 Integrated machine learning architectures for a knowledge graph embeddings (KGEs) approach - p. 139 15. 9 Future trends and directions for knowledge graph embeddings based on visualization methodologies - p. 159 16. 10 A brief study on evaluation metrics for knowledge graph embeddings - p. 177 17. 11 Design, construction, and recent advancements in temporal knowledge graphs for automated driving - p. 205 18. 12 Knowledge graph-based question answering (KG-QA) using natural language processing - p. 227 19. 13 An integrated framework for knowledge graphs based on battery management - p. 251 20. 14 Ontology-based information integration standards for the automotive industry - p. 273 21. 15 Emerging graphical data management methodologies for automated driving - p. 287 22. 16 Knowledge graphs vs collision avoidance systems: Pros and cons - p. 311 23. 17 Autonomous vehicle collision prediction systems: Artificial intelligence (AI) in action with knowledge graphs - p. 335 24. 18 Risk assessment based on dynamic behavior for autonomous systems using knowledge graphs - p. 359 25. 19 Case studies on knowledge graphs in automated driving - p. 377 26. Index - p. 395
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.
1. Knowledge Graph-Based Methods for Automated Driving - p. i 2. Cover image - p. i 3. Title page - p. i 4. Table of Contents - p. i 5. Copyright - p. iv 6. Contributors - p. xiii 7. 1 Knowledge graph-based method for self-driving cars in Industry 5.0 - p. 1 8. 2 An overview of knowledge representation learning based on ER knowledge graph - p. 19 9. 3 Emerging technologies and tools for knowledge gathering in automated driving - p. 35 10. 4 Safety regulations and standards for automated driving - p. 59 11. 5 Reliability and ethics developments in knowledge graphs for automated driving - p. 79 12. 6 Role of knowledge graph-based methods in human—AI systems for automated driving - p. 99 13. 7 Knowledge-infused learning: A roadmap to autonomous vehicles - p. 119 14. 8 Integrated machine learning architectures for a knowledge graph embeddings (KGEs) approach - p. 139 15. 9 Future trends and directions for knowledge graph embeddings based on visualization methodologies - p. 159 16. 10 A brief study on evaluation metrics for knowledge graph embeddings - p. 177 17. 11 Design, construction, and recent advancements in temporal knowledge graphs for automated driving - p. 205 18. 12 Knowledge graph-based question answering (KG-QA) using natural language processing - p. 227 19. 13 An integrated framework for knowledge graphs based on battery management - p. 251 20. 14 Ontology-based information integration standards for the automotive industry - p. 273 21. 15 Emerging graphical data management methodologies for automated driving - p. 287 22. 16 Knowledge graphs vs collision avoidance systems: Pros and cons - p. 311 23. 17 Autonomous vehicle collision prediction systems: Artificial intelligence (AI) in action with knowledge graphs - p. 335 24. 18 Risk assessment based on dynamic behavior for autonomous systems using knowledge graphs - p. 359 25. 19 Case studies on knowledge graphs in automated driving - p. 377 26. Index - p. 395
