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Knowledge Graph-Based Methods for Automated Driving
- 1st Edition - April 1, 2025
- Editors: Manar Mohaisen, Malathy Sathyamoorthy, M. Nalini, Rajesh Kumar Dhanaraj
- 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 approaches.… 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.
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 understanding of the current challenges
- Analyses 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
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 1, 2025
- Imprint: Elsevier
- Paperback ISBN: 9780443300400
- eBook ISBN: 9780443300417