Knowledge Graph-Based Methods for Automated Driving

1st Edition - April 11, 2025
Imprint: Elsevier
Editors: Rajesh Kumar Dhanaraj, M. Nalini, Malathy Sathyamoorthy, Manar Mohaisen
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 ap… Read more

Knowledge Graph-Based Methods for Automated Driving

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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.

Knowledge Graph-Based Methods for Automated Driving
Cover image
Title page
Table of Contents
Copyright
Contributors
1 Knowledge graph-based method for self-driving cars in Industry 5.0
Abstract
1.1 Introduction
1.2 Graphs: What are they?
1.3 The concept of properties graphs
1.4 Knowledge graphs presented
1.5 Self-driving cars
1.6 Mechanisms of self-driving cars
1.7 Industry 5.0
1.8 Importance of KG for self-driving cars in Industry 5.0
1.9 Reinforcement learning
1.10 RL for KG
1.10.1 RL’s initial work
1.10.2 Initial preparations for RL-KG
1.10.3 MDP in KG
1.11 Blockchain innovation combined with intelligent computing to develop self-driving cars with KG
1.12 Conclusion
References
2 An overview of knowledge representation learning based on ER knowledge graph
Abstract
2.1 Introduction
2.1.1 Latest research/recent developments in knowledge graphs
2.1.2 Objectives of the chapter
2.1.3 Structure of this chapter
2.2 Autonomous driving in smart cities
2.3 Knowledge graphs: Foundation for autonomous driving
2.4 Knowledge graphs in autonomous driving and their applications
2.5 Traffic congestion prediction and mitigation in smart cities: Knowledge graphs and machine learning
2.6 Object detection in complex environments, sensor technologies and fusion, and deep learning approaches
2.7 Collision avoidance strategies: Detection and prediction
2.8 Machine learning for real-time decision-making
2.9 Conclusion and future scope
References
3 Emerging technologies and tools for knowledge gathering in automated driving
Abstract
3.1 Overview
3.1.1 Overview of knowledge gathering in automated driving
3.1.2 State-of-the-art technologies
3.1.3 Overview of automated driving systems
3.1.4 Challenges in knowledge acquisition
3.2 Sensor technologies for environmental perception
3.2.1 Vehicle sensors
3.2.2 Perception sensor
3.2.3 Sensor fusion
3.3 Data collection and frameworks
3.3.1 Cognition
3.3.2 Memory
3.3.3 Planning
3.3.4 Reflection
3.4 Machine learning and AI for knowledge extraction
3.4.1 Regression algorithms
3.4.2 Pattern recognition algorithms
3.4.3 Clustering
3.4.4 Decision matrix algorithms
3.5 Simulation and virtual testing environments
3.5.1 Simulation platforms
3.5.2 Simulation benefits and challenges
3.6 Human-centered autonomous vehicle systems
3.6.1 Shared autonomy
3.6.2 Learn from data
3.6.3 Human sensing
3.6.4 Shared perception control
3.6.5 Deep personalization
3.6.6 Imperfect by design
3.6.7 System-level experience
3.7 Regulatory and ethical implications
3.7.1 Legal dimensions
3.7.2 Ethical considerations
3.7.3 Social impacts
3.8 Future directions and recommendations
3.8.1 Collaborative stakeholder engagement
3.8.2 Ethical AI integration and public awareness
3.8.3 Ethical and responsible AI research
3.8.4 Pilot programs and progressive implementation
References
4 Safety regulations and standards for automated driving
Abstract
4.1 Introduction
4.2 Safety regulations for automated driving
4.2.1 Ensuring public safety in automated driving
4.2.2 Ethical considerations in automated driving
4.2.3 Mitigating cybersecurity risks in automated driving
4.2.4 Encouraging responsibility and liability
4.2.5 Encouraging innovation and development
4.3 Safety standards for automated driving
4.3.1 ISO 26262
4.3.2 ISO/SAE 21434
4.3.3 ISO/PAS 21448
4.3.4 ISO 34503
4.3.5 SAE J3016
4.3.6 ANSI/UL 4600
4.3.7 IEEE P2846
4.3.8 NHTSA
4.3.9 UNECE WP.29
4.3.10 ITU-T
4.4 Guidelines for safety regulations and standards for automated driving
4.4.1 Recognize applicable regulations
4.4.2 Determine safety requirements
4.4.3 Create a safety management system
4.4.4 Conduct hazard analysis and risk assessment
4.4.5 Define the performance metrics and safety goals
4.4.6 Implement safety measures and controls in place
4.4.7 Validate and verify safety criteria
4.4.8 Document compliance and traceability
4.4.9 Stay informed and updated
4.4.10 Cultivate a safety culture
4.5 Conclusion
References
5 Reliability and ethics developments in knowledge graphs for automated driving
Abstract
5.1 Introduction
5.1.1 Understanding knowledge graphs
5.1.2 Importance of knowledge graphs in automated driving
5.1.3 Overview of reliability and ethical challenges
5.1.4 Fundamentals of reliability in knowledge graphs
5.1.5 Reliability metrics and measurements
5.1.6 Challenges in ensuring reliability in knowledge graphs
5.1.7 Techniques for improving reliability in knowledge graphs
5.1.8 Ethical considerations in knowledge graphs for automated driving
5.1.9 Ethical framework for anonymous systems
5.1.10 Privacy and consent
5.1.11 Security and integrity
5.1.12 Transparency and accountability
5.1.13 Fairness and inclusivity
5.1.14 Legal and regulatory compliance
5.1.15 Societal impact and ethical use
5.1.16 Moral decision-making in anonymous vehicles
5.1.17 Algorithmic ethics
5.1.18 Utilitarian vs deontological approaches
5.1.19 Ethical transparency
5.1.20 Adaptability and learning
5.1.21 Public input and stakeholder involvement
5.1.22 Legal and regulatory compliance
5.1.23 Addressing ethical dilemmas through knowledge graphs
5.1.24 Comprehensive information integration
5.1.25 Contextual understanding
5.1.26 Real-time updates and education
5.1.27 Explainability and transparency
5.1.28 Participation of multiple stakeholders
5.1.29 Risk evaluation and reduction
5.1.30 Advances in reliability assurance techniques
5.1.31 Machine learning approaches to reliability assurance
5.1.32 Formal verification methods
5.1.33 Testing and validation strategies
5.1.34 Ensuring transparency and accountability
5.1.35 Explainability in knowledge graphs
5.1.36 Auditing and monitoring systems
5.1.37 Legal and regulatory implications
5.2 Benefits, limitations, and future prospects
5.2.1 New developments in knowledge graphs for automated vehicles
5.2.2 Upcoming moral difficulties
5.2.3 Suggestions for additional research and development
5.3 Conclusion
References
6 Role of knowledge graph-based methods in human—AI systems for automated driving
Abstract
6.1 Introduction
6.2 Background
6.2.1 AVs at a glance
6.2.2 Existing issues
6.2.3 AVs regulations and standards
6.3 Explanations for autonomous driving
6.3.1 Potential benefits of explanations in AVs
6.3.2 Explanation recipients in AVs
6.3.3 How to deliver explanations in AVs?
6.4 AI for autonomous driving
6.4.1 Convolutional neural networks
6.4.2 Reinforcement learning
6.5 Datasets, ontologies, and knowledge graphs
6.5.1 nuScenes
6.5.2 BDD
6.5.3 PandaSet
6.5.4 Adding other datasets
6.5.5 Global knowledge graph
6.6 Conclusions and future directions
References
7 Knowledge-infused learning: A roadmap to autonomous vehicles
Abstract
7.1 Introduction
7.1.1 Background and significance
7.1.2 Challenges faced by autonomous cars
7.1.3 Insufficiency of traditional machine learning in real-world scenarios
7.2 Knowledge-infused learning
7.2.1 Definition of knowledge-infused learning
7.2.2 Key concepts of knowledge-infused learning
7.2.3 Domain-specific adaptability
7.3 Roadmap to knowledge-infused learning
7.3.1 The need for integration of relevant knowledge
7.3.2 Sensor data integration
7.3.3 Semantic mapping
7.3.4 Knowledge representation
7.4 Safety and efficiency enhancement
7.4.1 Injection of domain-specific information
7.4.2 Comprehension of surroundings
7.4.3 Proactive anticipation and adaptation
7.5 Learning continuum for autonomous cars
7.5.1 Adaptation to changing environments
7.5.2 Communal learning process through real-world encounters
7.6 Benefits, limitations, and future prospects
7.6.1 Knowledge acquisition
7.6.2 Implications for the future
7.6.3 Envisioning a future of safer, smarter, and automated transportation
References
8 Integrated machine learning architectures for a knowledge graph embeddings (KGEs) approach
Abstract
8.1 Introduction
8.1.1 The connection between knowledge graphs and machine learning
8.2 How does knowledge graph deal with this challenge?
8.3 What exactly is a graph? They are EVERYWHERE!
8.4 How can graphs be used in ML?
8.5 Background
8.6 The integration of large language models and knowledge graphs
8.6.1 Large language models
8.7 Several approaches facilitate this integration
8.7.1 Enhancing LLMs with knowledge graphs
8.7.2 Using language models to enrich knowledge graphs
8.7.3 Hybrid systems
8.8 An algorithm for the integration of machine learning (ML) architectures into knowledge graph embeddings
8.9 Different architectures and several key attributes
8.9.1 Case studies and practical applications
8.10 Conclusion
References
9 Future trends and directions for knowledge graph embeddings based on visualization methodologies
Abstract
9.1 Foundations and implications of knowledge graph embeddings
9.2 Importance of visualization methodologies in understanding complex relationships
9.2.1 Intuitive representation
9.2.2 Exploration and analysis
9.2.3 Identification of patterns and trends
9.2.4 Communication and collaboration
9.2.5 Integration with knowledge graph embeddings
9.3 Knowledge graph embedding: Foundations and techniques
9.3.1 Overview
9.3.2 Techniques for generating embeddings
9.3.3 Challenges and limitations
9.4 Visualization methodologies for knowledge graphs
9.4.1 Importance of visualization in knowledge graph analysis
9.4.2 Common visualization techniques and tools
9.5 Emerging trends in knowledge graph embeddings
9.5.1 Embedding diversity: Capturing richer contextual information
9.5.2 Dynamic embedding: Adapting to changing knowledge graphs
9.5.3 Hierarchical embedding: Modeling intricate knowledge hierarchies
9.5.4 Interpretable embedding and privacy-protective techniques
9.6 Interactive visualization tools for knowledge graphs
9.6.1 Role of interactive visualization in knowledge graph exploration
9.6.2 Overview of existing interactive visualization tools
9.6.3 Case studies demonstrating the effectiveness of visualization dashboard
9.7 Scalability challenges and solutions
9.7.1 Scalability issues in handling large and dynamic knowledge graphs
9.7.2 Techniques for improving scalability in knowledge graph embeddings and visualization
9.8 Cross-modal embeddings in knowledge graphs
9.8.1 Introduction to cross-modal embeddings
9.9 Case studies and applications
9.9.1 Real-world applications of knowledge graph embeddings and visualization methodologies
9.9.2 Case studies showcasing successful implementations and their impact
9.10 Future directions and research opportunities
9.10.1 Potential areas for further research and development
9.10.2 Anticipated advancements in knowledge graph embeddings and visualization methodologies
9.11 Conclusion
9.11.1 Summary of key findings and insights
9.11.2 Final remarks on the future of knowledge graph embeddings and visualization
References
10 A brief study on evaluation metrics for knowledge graph embeddings
Abstract
10.1 Introduction
10.1.1 Background and motivation
10.1.2 Significance of KGEs
10.1.3 Purpose of evaluation metrics
10.2 KGE: A brief overview
10.2.1 Models for KG embedding
10.2.2 Characteristics of KG embedding
10.3 Commonly used evaluation metrics
10.3.1 Importance of evaluation metrics
10.3.2 Challenges in evaluating KGEs
10.3.3 Comparison with traditional embedding models and advantages
10.3.4 Link prediction metrics
10.3.5 Importance of mean rank and mean reciprocal rank in link prediction
10.4 Additional evaluation metrics
10.4.1 Clustering metrics
10.4.2 Metrics specific to KGE
10.4.3 Significance in model-specific evaluation
10.5 Latest research in KGE evaluation techniques
10.5.1 Improving quality with new evaluation frameworks
10.5.2 Handling the multirelational nature of the graphs
10.5.3 Evaluating the KGE model for activities related to knowledge enhancement downstream (Fei et al., 2021)
10.6 Applications of evaluation metrics
10.6.1 Quantitative evaluation metrics for KGE applications
10.6.2 Techniques for qualitative evaluation
References
11 Design, construction, and recent advancements in temporal knowledge graphs for automated driving
Abstract
11.1 Introduction
11.2 Background
11.3 Temporal knowledge graphs
11.3.1 Entities and relationships
11.3.2 Temporal annotations
11.3.3 Dynamic evolution
11.3.4 Historical, present, and predictive data
11.3.5 Temporal granularity and resolution
11.3.6 Incorporation of uncertainty
11.4 Designing a TKG for automated driving
11.4.1 Data collection
11.4.2 Data preprocessing
11.4.3 Sensor fusion
11.4.4 Temporal representation
11.4.5 Ensuring data quality and consistency
11.4.6 Integration into the TKG
11.5 Construction of the TKG
11.5.1 Data collection
11.5.2 Data preprocessing
11.5.3 Sensor fusion
11.5.4 Temporal representation
11.5.5 Ensuring data quality and consistency
11.5.6 Integration into the TKG
11.6 Recent advancements in TKGs
11.6.1 Enhanced prediction capabilities
11.6.2 Anomaly detection
11.6.3 Decision support systems
11.6.4 Adaptive, context-aware algorithms
11.7 Conclusion
References
12 Knowledge graph-based question answering (KG-QA) using natural language processing
Abstract
12.1 Learning and knowledge outcomes
12.1.1 Data preparation
12.1.2 Preprocessing
12.1.3 Feature extraction
12.1.4 Model training
12.1.5 Inference
12.1.6 Evaluation
12.1.7 Iterative improvement
12.1.8 Deployment
12.2 Introduction
12.2.1 Overview of KG-QA
12.2.2 Various aspects of knowledge graph-based question-answering framework
12.2.3 Challenges in current question-answering methods
12.2.4 Objective of the chapter
12.3 The role of NLP in KG-QA
12.4 Knowledge graphs in question answering
12.5 Problems faced by KG-QA
12.6 Knowledge graphs in NLP
12.7 Importance of KG-QA
12.8 Merging knowledge graphs with NLP
12.9 Mathematical representation for (KG-QA) using NLP
12.10 Proposed method for KG-QA using NLP
12.11 Data sets for KG-QA
12.11.1 Knowledge graph data
12.11.2 Question-answering datasets
12.11.3 Diagram datasets
12.12 Case reports and real-world implementations
12.13 Barriers and moral concerns
12.13.1 Data privacy and security
12.13.2 Bias in knowledge representation
12.13.3 Language understanding and ambiguity
12.13.4 Algorithmic fairness and transparency
12.13.5 User understanding and trust
12.14 Conclusion
References
13 An integrated framework for knowledge graphs based on battery management
Abstract
13.1 Introduction
13.2 Background
13.3 Knowledge graph
13.3.1 Representation of a directed graph as a three-way array using KG
13.3.2 Importance of knowledge graph
13.4 KGs based on battery management
13.4.1 Entities in the KG
13.4.2 Relationships
13.4.3 Attributes and properties
13.4.4 Semantic modeling
13.4.5 Graph-based representation
13.5 Popular technologies used in EV battery management
13.5.1 Semantic modeling
13.5.2 Graph-based querying mechanisms
13.5.3 Data integration
13.5.4 Telematics and IoT
13.5.5 Predictive analytics
13.6 Proposed framework for EV battery management
13.7 Result analysis
13.7.1 Identifying data sources
13.7.2 Centralized repository creation
13.7.3 Semantic modeling
13.7.4 Graph-based representations
13.7.5 Incorporating battery specifications
13.7.6 Including dynamic factors
13.7.7 Enabling holistic insights
13.8 Case study: Comparative analysis of Exide Industries and Amara Raja Batteries
13.8.1 Exide Industries implementation
13.8.2 Amara Raja Batteries implementation
13.8.3 Analysis
13.8.4 Comparative results
13.9 Conclusion
References
14 Ontology-based information integration standards for the automotive industry
Abstract
14.1 Introduction
14.1.1 Key objectives of this research include
14.2 Foundation of ontology
14.2.1 The definitions of ontology
14.2.2 Ontology-based data integration
14.3 Ontology learning approaches
14.4 Learning from unstructured data
14.4.1 Text mining and natural language processing (NLP)
14.4.2 Image and video analysis
14.5 Ontology integration process
14.6 Popular technology
14.6.1 Semantic web technologies
14.6.2 Linked data
14.6.3 SPARQL query language
14.6.4 Graph databases
14.6.5 Natural language processing (NLP)
14.6.6 Blockchain for data sharing
14.7 Case study of ontology-based Activa two-wheeler
14.7.1 Ontology development
14.7.2 Data integration
14.7.3 Semantic interoperability
14.7.4 Standards compliance
14.8 Conclusion
References
15 Emerging graphical data management methodologies for automated driving
Abstract
15.1 Introduction
15.2 Fundamentals of AuD data
15.2.1 Environmental perception
15.2.2 V2X
15.2.3 High-definition map
15.2.4 Planning decision
15.2.5 Vehicle network bus technology
15.2.6 Information security and privacy protection technology
15.3 Graphical data representation in AuD
15.3.1 Data layer
15.3.2 Knowledge layer
15.3.3 Application layer
15.3.4 Dataset, ontology, and knowledge graphs
15.4 Current graph database management techniques
15.4.1 Neo4j (neo technology)
15.4.2 Infinite graph
15.4.3 DEX
15.4.4 HyperGraphDB
15.4.5 Infogrid
15.4.6 Titan
15.4.7 Trinity
15.5 Application of knowledge graph to AuD
15.5.1 Object detection
15.5.2 Semantic segmentation
15.5.3 Mapping
15.5.4 Scene understanding
15.5.5 Object behavior prediction
15.5.6 Motion planning
15.5.7 Validation
15.5.8 Future scope
15.6 Conclusions
References
16 Knowledge graphs vs collision avoidance systems: Pros and cons
Abstract
16.1 Introduction
16.2 Background
16.2.1 Knowledge graph
16.2.2 Evolution of knowledge graph
16.3 The DIKW pyramid
16.3.1 Data (raw, unprocessed facts)
16.3.2 Information (processed data)
16.3.3 Knowledge (recognition of patterns, relationships)
16.3.4 Wisdom (strategic actions and decisions)
16.4 Significance of knowledge graphs
16.4.1 Complex traffic patterns
16.4.2 Diverse driving rules and behavior
16.4.3 Linguistic nuances
16.4.4 Environmental and seasonal changes
16.5 Knowledge graph applications
16.5.1 Data collection and integration
16.5.2 Modeling regional specifics
16.5.3 Integration with navigation systems
16.6 Collision avoidance systems
16.6.1 History of collision avoidance systems
16.7 Significance of collision avoidance systems
16.7.1 High traffic density
16.7.2 Diverse traffic participants
16.7.3 Unpredictable driving behavior
16.7.4 Road safety
16.8 CAS in traffic conditions
16.8.1 Integration with ADAS
16.8.2 Adaptive cruise control
16.8.3 Lane-keeping assist
16.8.4 Traffic sign recognition
16.9 Integration of CAS with knowledge graphs
16.9.1 Integration works
16.9.2 Features of integration of CAS and KG
16.10 Comparative analysis of knowledge graphs and collision avoidance systems
16.11 Knowledge graphs and collision avoidance systems: Pros and cons
16.11.1 Knowledge graphs pros and cons
16.11.2 Knowledge graphs cons
16.11.3 Collision avoidance systems pros and cons
16.12 Conclusion
References
17 Autonomous vehicle collision prediction systems: Artificial intelligence (AI) in action with knowledge graphs
Abstract
17.1 Introduction
17.1.1 Overview of autonomous vehicles
17.2 Importance of collision prediction in AVs
17.3 Challenges in current collision prediction methods
17.4 Objective of the chapter
17.5 The role of AI in AVs
17.6 Limitations and challenges faced by AVs
17.7 Advanced collision prediction: The need
17.8 Importance of real-time decision-making
17.9 Knowledge graphs in AVs
17.9.1 Introduction to knowledge graph
17.10 Integration of knowledge graphs into AV systems
17.11 Quantum computing in AVs
17.12 Integrating AI, knowledge graphs, and quantum computing
17.13 Datasets
17.14 Case studies and practical applications
17.14.1 Real-world implementations
17.15 Challenges and ethical considerations
17.15.1 Technical challenges and limitations
17.15.2 Ethical and regulatory issues
17.15.3 Safety and privacy concerns
17.16 Conclusion
References
18 Risk assessment based on dynamic behavior for autonomous systems using knowledge graphs
Abstract
18.1 Introduction
18.2 Background and motivation
18.2.1 Current status of autonomous vehicles for two-wheeler
18.2.2 Risk management
18.3 Knowledge graph
18.3.1 Importance of knowledge graph for two-wheelers
18.4 Proposed methodology of autonomous systems
18.4.1 Data preprocessing
18.4.2 Knowledge graph integration
18.4.3 Dynamic behavior modeling
18.4.4 Machine learning algorithms
18.4.5 Physics-based simulation
18.4.6 Decision support system
18.4.7 Using knowledge graph
18.4.8 Adaptive responses
18.4.9 Recommendation
18.5 Result analysis
18.5.1 Data collection
18.5.2 Collect real-time data
18.5.3 External sources
18.5.4 Data preprocessing
18.5.5 Knowledge graph integration
18.5.6 Dynamic behavior modeling
18.5.7 Decision support system
18.6 Case study: Comparative study of Hero and Honda two-wheeler Company
18.7 Conclusion
References
19 Case studies on knowledge graphs in automated driving
Abstract
19.1 Introduction
19.1.1 Key characteristics of knowledge graph
19.1.2 Challenges of KG-based approaches
19.2 The role of knowledge graphs in automated driving
19.3 Knowledge graph use cases in automated driving
19.3.1 Predicting traffic flow with KG-based route planning
19.3.2 Enhancing scene understanding with KG-based object recognition
19.3.3 Autonomous navigation in urban environments
19.3.4 Personalized driving assistance with KG-based driver modeling
19.3.5 Dynamic route planning with KGs
19.3.6 Safe interaction with vulnerable road users
19.4 Conclusion
References
Index

Rajesh Kumar Dhanaraj

Dr. Rajesh Kumar Dhanaraj is a professor at the Symbiosis International (Deemed University) in Pune, India. His research and publication interests include cyber-physical systems, wireless sensor networks, and cloud computing. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the Computer Science Teacher Association (CSTA) and member of the International Association of Engineers (IAENG). He is an expert advisory panel member of Texas Instruments Inc. (USA), and an associate editor of International Journal of Pervasive Computing and Communications (Emerald Publishing).

Affiliations and expertise

Professor, Symbiosis International (Deemed University), Pune, India

M. Nalini

Dr. Nalini holds a PhD in Electronics and Communication Engineering from Sri Chandrasekarendra Saraswathi Viswa Maha Vidyalaya, Kanchipuram, India. Her research and publication interests include Artificial Intelligence, biomedical engineering, wireless sensor networks, and Internet of Things. She holds two patents in India and has received a grant to submit another application through the AICTE Quality Improvement Schemes supported by the Gvt. of India.

Affiliations and expertise

Professor, Department of Electronics and Instrumentation Engineering, Sri Sairam Engineering College, Chennai, Tamil Nadu, India

Malathy Sathyamoorthy

Dr. Malathy holds a PhD in Information and Communication Engineering from Anna University, Chennai, India. Her research areas include wireless sensor networks, Internet of Things, and applied machine learning. She is a life member of the Indian Society for Technical Education (ISTE) and the International Association of Engineers (IAENG). She is an active author/editor for Springer, CRC Press, and Elsevier. She is also a reviewer for Wireless Networks (Springer) and on the editorial board at many international conferences.

Affiliations and expertise

Assistant Professor, Department of Information Technology, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India

Manar Mohaisen

Dr. Mohaisen received a Master’s degree in Communications and Signal Processing from the University of Nice Sophia Antipolis, Nice, France, in 2005, and a PhD in Communications Engineering from Inha University, Incheon, South Korea, in 2010. From 2001 to 2004, he worked as a Cell Planning Engineer with the Palestinian Telecommunications Company, Nablus, Palestine. From 2010 to 2019, he was a full-time Lecturer and an Assistant Professor with the Dept. of EEC Engineering Korea Tech, Cheonan, South Korea. He is currently an Associate Professor with the Department of Computer Science, Northeastern Illinois University, Chicago, IL, USA. His research interests include wireless communications with a focus on MIMO systems, systems and internet security, AI applications to security, and social network analysis.

Affiliations and expertise

Associate Professor, Department of Computer Science, Northeastern Illinois University, Chicago, IL, USA

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Knowledge Graph-Based Methods for Automated Driving

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Rajesh Kumar Dhanaraj

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Manar Mohaisen

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