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Full Description
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.
Contents
1. Knowledge graph-based methods for automated driving
2. An overview of knowledge representation learning based on ER knowledge graph
3. Emerging technologies and tools for knowledge gathering in automated driving
4. Awareness of safety regulations and standards for automated driving
5. Reliability and ethics developments in knowledge graphs for automated driving
6. Role of knowledge graph-based methods in human-AI systems for automated driving
7. Knowledge-infused learning: A roadmap to autonomous vehicles
8. Integrated machine learning architectures for a knowledge graph embeddings (KGEs) approach
9. Future trends and directions for knowledge graph embeddings based on visualization methodologies
10. A brief study on evaluation metrics for knowledge graph embeddings
11. Design, construction, and recent advancements in temporal knowledge graph for automated
driving
12. Knowledge graph-based question answering (KG-QA) using natural language processing
13. An integrated framework for knowledge graphs based on battery management
14. Ontology-based information integration standards for the automotive industry
15. Emerging graphical data management methodologies for automated driving
16. Knowledge graphs vs collision avoidance systems: Pros and cons
17. Autonomous vehicle collision prediction systems: AI in action with knowledge graphs
18. Risk assessment based on dynamic behavior for autonomous systems using knowledge graphs
19. Case studies on knowledge graphs in automated driving
