Knowledge Graph-Based Methods for Automated Driving
Published on 11. April 2025
400 pages
978-0-443-30041-7 (ISBN)
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.
- 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
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Rajesh Kumar Dhanaraj | M. Nalini | Malathy Sathyamoorthy
Knowledge Graph-Based Methods for Automated Driving
Book
05/2025
Elsevier
€231.49
Shipment within 15-20 days
Content
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
