Knowledge Graph-Based Methods for Automated Driving
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Knowledge Graph-Based Methods for Automated Driving
Dhanaraj, Rajesh Kumar; Nalini, M.; Sathyamoorthy, Malathy; Mohaisen, Manar
Elsevier - Health Sciences Division
04/2025
400
Mole
Inglês
9780443300400
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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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Automated driving; automated vehicles; knowledge graphs; knowledge graph embeddings; graph neural networks; graph-structured data; automotive engineering; autonomous vehicle systems; integration of driving datasets; automotive ontologies; IoT-enabled autonomous vehicles; intelligent transport engineering; autonomous vehicle dynamics and control; intelligent transport systems; control and estimation problems; autonomous navigation; artificial intelligence applications; deep learning methods; knowledge graph-based methods; information networks; reliability and safety of automated systems; structured, dynamic, and relational data; machine learning algorithms; perception; situation comprehension; scene understanding; object behavior prediction; motion planning; validation; reasoning; relation extraction; representation learning; human-AI systems for automated driving; autonomous vehicle collision prediction systems; ADS safety performance
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
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
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Automated driving; automated vehicles; knowledge graphs; knowledge graph embeddings; graph neural networks; graph-structured data; automotive engineering; autonomous vehicle systems; integration of driving datasets; automotive ontologies; IoT-enabled autonomous vehicles; intelligent transport engineering; autonomous vehicle dynamics and control; intelligent transport systems; control and estimation problems; autonomous navigation; artificial intelligence applications; deep learning methods; knowledge graph-based methods; information networks; reliability and safety of automated systems; structured, dynamic, and relational data; machine learning algorithms; perception; situation comprehension; scene understanding; object behavior prediction; motion planning; validation; reasoning; relation extraction; representation learning; human-AI systems for automated driving; autonomous vehicle collision prediction systems; ADS safety performance
