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For Authors
Submit a ProposalAutonomous Systems in the Internet of Vehicles
One Line Description
Accelerate your expertise in the future of autonomous navigation by mastering essential fusion algorithms that enable vehicles to operate safely and reliably in complex, dynamic environments.
Audience
Researchers, engineers, developers, policymakers, and industry professionals working in the fields of autonomous vehicles, computer vision, sensor fusion, and connected vehicles.
Researchers, engineers, developers, policymakers, and industry professionals working in the fields of autonomous vehicles, computer vision, sensor fusion, and connected vehicles.
Description
Advancements in sensor technology have enabled autonomous systems to operate efficiently and safely in the Internet of Vehicles environment. Multisensor image fusion is a crucial component in enhancing the capabilities of these autonomous systems by combining information from multiple sensors such as cameras, LiDAR, radar, and ultrasonic sensors. This book delves into the role of multisensor image fusion in the Internet of Vehicles for autonomous systems. It will cover the fundamental concepts of multisensor image fusion, different fusion methods, and their applications in autonomous systems for the IoV. It will also address the challenges associated with multisensor fusion, such as sensor calibration, synchronization, and noise reduction and discuss the benefits of multisensor fusion in improving object detection, tracking, and decision-making processes in autonomous vehicles operating in the IoV. This book is a comprehensive overview of multisensor image fusion in the context of IoV for autonomous systems, highlighting its importance in achieving reliable and robust autonomous navigation in dynamic and complex environments.
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Advancements in sensor technology have enabled autonomous systems to operate efficiently and safely in the Internet of Vehicles environment. Multisensor image fusion is a crucial component in enhancing the capabilities of these autonomous systems by combining information from multiple sensors such as cameras, LiDAR, radar, and ultrasonic sensors. This book delves into the role of multisensor image fusion in the Internet of Vehicles for autonomous systems. It will cover the fundamental concepts of multisensor image fusion, different fusion methods, and their applications in autonomous systems for the IoV. It will also address the challenges associated with multisensor fusion, such as sensor calibration, synchronization, and noise reduction and discuss the benefits of multisensor fusion in improving object detection, tracking, and decision-making processes in autonomous vehicles operating in the IoV. This book is a comprehensive overview of multisensor image fusion in the context of IoV for autonomous systems, highlighting its importance in achieving reliable and robust autonomous navigation in dynamic and complex environments.
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Author / Editor Details
Balamurugan Balusamy, PhD is an Associate Dean at Shiv Nadar University with more than 12 years of experience. He has published more than 200 papers in international journals and edited and authored more than 80 books. His research focuses on engineering education, blockchain, and data sciences.
Balamurugan Balusamy, PhD is an Associate Dean at Shiv Nadar University with more than 12 years of experience. He has published more than 200 papers in international journals and edited and authored more than 80 books. His research focuses on engineering education, blockchain, and data sciences.
Sandeep Kumar Mathivanan, PhD is an Assistant Professor in the School of Computer Science and Engineering at Galgotias University with more than six years of research experience. He is a reviewer for a number of international journals and conferences. His research interests include machine learning, deep learning, remote sensing, and big data.
Prabhu Jayagopal, PhD is a Professor in the Department of Software and Systems Engineering in the School of Computer Science, Engineering, and Information Systems at the Vellore Institute of Technology. He has published 104 papers in international journals, book chapters, and conferences. His research interests include machine learning, artificial intelligence, and IoT related to healthcare.
S.K.B. Sangeetha, PhD is a Senior Assistant Professor in the Department of Computer Science and Engineering at the SRM Institute of Science and Technology with more than 15 years of teaching experience. She has published more than 75 research articles, ten book chapters in peer-reviewed international journals, and ten patents. She is a lifetime member of the International Society for Technology in Education and the International Education Initiative.
Ali Kashif Bashir, PhD is a Professor of Networks and Security at Manchester Metropolitan University. He is also affiliated with the University of Electronic Science and Technology of China, National University of Science and Technology, Pakistan, and University of Guelph. He has delivered more than 30 talks across the globe, organized more than 40 guest editorials, and chaired 35 conferences and workshops.
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Table of Contents
Preface
1. A Cognitive Edge-Driven Autonomous Learning System for Scalable and Secure IoV Automation
V. Muthukumaran, S. Satheesh Kumar, Jahnavi S., Rose Bindu Joseph P. and Firoz Khan
1.1 Introduction
1.2 Related Study
1.3 System Methodology
1.3.1 Multilayer Edge Computing Framework
1.3.2 Federated Reinforcement Learning Model
1.3.3 Adaptive Dynamic Power Control Algorithm for CEALS
1.4 Experimentation Results
1.5 Conclusion
References
2. Adaptive Feature Alignment and Fusion for Multisensor Image Integration in the Internet of Vehicles
Vijay Anand R. and Madala Guru Brahmam
2.1 Introduction
2.2 Related Study
2.3 System Methodology
2.3.1 Multisensor Data Acquisition
2.3.2 Preprocessing
2.3.3 Dynamic Feature Alignment in AFAF-Net
2.3.4 Attention-Guided Fusion Method
2.3.5 Real-Time Object Detection
2.4 Experimentation Results
2.5 Conclusion
References
3. Design of ML-CASF: Multilayer Context-Aware Sensor Fusion for Autonomous Vehicles in the Internet of Vehicles
Sukumar R. and Sathishkumar V.E.
3.1 Introduction
3.2 Related Study
3.3 System Methodology
3.3.1 Sensor Data Acquisition
3.3.2 Preprocessing and Synchronization
3.3.3 Graph Construction for Sensor Data
3.4 Experimentation Results
3.5 Conclusion
References
4. Adaptive Multimodal Fusion for Robust Autonomous Driving Perception with Attention-Based Learning
Sangeetha R.
4.1 Introduction
4.2 Related Study
4.3 System Methodology
4.3.1 Data Collection and Preprocessing
4.3.2 Feature Extraction
4.3.3 Proposed Methodology
4.4 Experimentation Results
4.4.1 Performance Analysis
4.4.2 Computational Performance Comparison
4.4.3 Impact of Sensor Modalities on Detection Performance
4.5 Conclusion
References
5. Optimization-Driven Multisensor Fusion Framework for Autonomous Systems in the Internet of Vehicles
C. Gowdham, A.B. Hajira Be, C. Ashwini, S. Prabu and Zubair Rahaman
5.1 Introduction
5.2 Related Study
5.3 System Methodology
5.3.1 Data Acquisition and Preprocessing
5.3.2 Proposed Framework
5.3.2.1 EKF for Sensor Fusion
5.3.2.2 PF for Nonlinear Fusion
5.3.2.3 Deep Learning–Based Fusion Using CNNs and Transformers
5.4 Experimentation Results
5.5 Conclusion
References
6. A Hybrid Neurosymbolic Decision-Making Approach with Multimodal Sensor Fusion for Autonomous Vehicles
Devi A., Rose Bindu Joseph P. and Meram Munirathnam
6.1 Introduction
6.2 Related Study
6.3 System Methodology
6.3.1 Perception Module
6.3.2 Hybrid Decision-Making Algorithm for AVs
6.3.3 Trajectory Planning and Execution
6.4 Experimentation Results
6.5 Conclusion
References
7. Reinforcement Learning–Driven Multisensor Fusion for Real-Time Navigation in Intelligent and Opportunistic Vehicular Networks
Mahalakshmi, Suma T., Soya Mathew and Nitya S.
7.1 Introduction
7.2 Related Study
7.3 System Methodology
7.3.1 Perception Module
7.3.2 Proposed Algorithms
7.4 Experimentation Results
7.5 Conclusion
References
8. Hybrid Multimodal Fusion Network (HMFNet) for Enhanced Perception in Autonomous Vehicles
Mahalakshmi, Ranjini K. S., Nidhi S. Vaishnaw and Jesla Joseph
8.1 Introduction
8.2 Related Study
8.3 System Methodology
8.3.1 Dataset Used
8.3.2 Feature Extraction
8.3.3 Proposed HMFNet
8.4 Experimentation Results
8.5 Conclusion
References
9. Fusion-Enhanced Adaptive Learning for Robust Multisensor Integration in Autonomous IoV
A. Radha Krishna, U.V. Ramesh, S. Sathish Kumar and Aimin Li
9.1 Introduction
9.2 Related Study
9.3 System Methodology
9.3.1 Data Acquisition and Sensor Integration
9.3.2 SESW Algorithm
9.3.3 Multiscale Spatiotemporal Fusion Network
9.3.3.1 Feature Extraction Layer
9.3.3.2 Multiscale Fusion Module
9.3.3.3 Decision Refinement Layer
9.3.4 Multitask Output for Perception, Localization, and Path Planning
9.3.5 Final Computation Flow
9.4 Experimentation Results
9.4.1 Localization Accuracy in Simulation
9.4.2 Object Detection and Perception Accuracy
9.4.3 Computational Efficiency and Processing Latency
9.4.4 Decision-Making Latency with V2X Simulation
9.4.5 Path Planning and Collision Avoidance in Simulation
9.5 Conclusion
References
10. Dynamically Reconfigurable Multisensor Fusion for Enhanced Object Detection in Autonomous Vehicles
V. Muthukumaran, M. Sathish Kumar, G. Kumaran, Vidya K.B. and Ahmad Alkhayyat
10.1 Introduction
10.2 Related Study
10.3 System Methodology
10.3.1 Data Acquisition and Preprocessing
10.3.2 Proposed Algorithms
10.4 Experimentation Results
10.5 Conclusion
References
11. AI-Driven Edge Computing for Secure and Efficient Internet of Vehicles (IoV) Communication
Sukumar R. and Saurav Mallik
11.1 Introduction
11.2 Related Study
11.3 System Methodology
11.3.1 Data Collection and Preprocessing
11.3.2 Feature Extraction
11.3.3 Proposed Algorithms
11.4 Experimentation Results
11.5 Conclusion
References
12. Federated Autoencoder-GRU–Based Intrusion Detection System for Secure IoV-Connected Autonomous Vehicles
Pegadapelli Srinivas, Vijey Nathan, Radhika Rajavelu, Suresh Kulandaivelu and Roger Atanga
12.1 Introduction
12.2 Background Study on IoV
12.3 System Methodology
12.3.1 Dataset Description
12.3.2 Data Preprocessing
12.3.3 Proposed Federated Autoencoder-GRU IDS
12.4 Experimental Results
12.5 Conclusion
References
13. Edge-Driven Multimodal Fusion Framework for Real-Time Emotion-Aware Vehicular Networks
Manjula Sanjay Koti, S. Satheesh Kumar, Janani S., Arun A. and Mahmoud Ahmad Al-Khasawneh
13.1 Introduction
13.2 Related Study
13.3 System Methodology
13.3.1 Multimodal Data Acquisition
13.3.2 Signal Preprocessing and Synchronization
13.3.3 Feature Extraction and Fusion
13.3.4 Emotion Recognition Engine
13.3.5 Emotional Readiness for Control Handover
13.4 Experimentation Results
13.5 Conclusion
References
14. Spatiotemporal Attention-Based CNN-BiLSTM Model for Robust Lane and Obstacle Detection in IoV-Enabled Autonomous Driving
Suresh Kulandaivelu, Syied Mazar, Sangeetha N., Sathiyapriya Rajavelu and Anita Garhwal
14.1 Introduction
14.2 Related Study
14.3 System Methodology
14.3.1 Dataset Used and Preprocessing
14.3.2 Network Architecture: Spatiotemporal Attention-Enhanced CNN-BiLSTM
14.3.3 Inference Optimization and Real-Time Deployment
14.4 Experimentation Results
14.5 Conclusion
References
15. Multimodal Vision-LiDAR Transformer Fusion for End-to-End IoV-Based Autonomous Navigation
Mohan Mani, Hariprasath K., C. Vijayakumar, Sathiyapriya Rajavelu and Sarawoot Boonkirdram
15.1 Introduction
15.2 Background Study
15.3 System Methodology
15.3.1 Simulation Environment and Dataset Generation
15.3.2 Multimodal Preprocessing Pipeline
15.3.3 Network Architecture: Transformer-Based Multimodal Fusion
15.4 Experimental Results
15.5 Conclusion
References
Index
Back to Top
Preface
1. A Cognitive Edge-Driven Autonomous Learning System for Scalable and Secure IoV Automation
V. Muthukumaran, S. Satheesh Kumar, Jahnavi S., Rose Bindu Joseph P. and Firoz Khan
1.1 Introduction
1.2 Related Study
1.3 System Methodology
1.3.1 Multilayer Edge Computing Framework
1.3.2 Federated Reinforcement Learning Model
1.3.3 Adaptive Dynamic Power Control Algorithm for CEALS
1.4 Experimentation Results
1.5 Conclusion
References
2. Adaptive Feature Alignment and Fusion for Multisensor Image Integration in the Internet of Vehicles
Vijay Anand R. and Madala Guru Brahmam
2.1 Introduction
2.2 Related Study
2.3 System Methodology
2.3.1 Multisensor Data Acquisition
2.3.2 Preprocessing
2.3.3 Dynamic Feature Alignment in AFAF-Net
2.3.4 Attention-Guided Fusion Method
2.3.5 Real-Time Object Detection
2.4 Experimentation Results
2.5 Conclusion
References
3. Design of ML-CASF: Multilayer Context-Aware Sensor Fusion for Autonomous Vehicles in the Internet of Vehicles
Sukumar R. and Sathishkumar V.E.
3.1 Introduction
3.2 Related Study
3.3 System Methodology
3.3.1 Sensor Data Acquisition
3.3.2 Preprocessing and Synchronization
3.3.3 Graph Construction for Sensor Data
3.4 Experimentation Results
3.5 Conclusion
References
4. Adaptive Multimodal Fusion for Robust Autonomous Driving Perception with Attention-Based Learning
Sangeetha R.
4.1 Introduction
4.2 Related Study
4.3 System Methodology
4.3.1 Data Collection and Preprocessing
4.3.2 Feature Extraction
4.3.3 Proposed Methodology
4.4 Experimentation Results
4.4.1 Performance Analysis
4.4.2 Computational Performance Comparison
4.4.3 Impact of Sensor Modalities on Detection Performance
4.5 Conclusion
References
5. Optimization-Driven Multisensor Fusion Framework for Autonomous Systems in the Internet of Vehicles
C. Gowdham, A.B. Hajira Be, C. Ashwini, S. Prabu and Zubair Rahaman
5.1 Introduction
5.2 Related Study
5.3 System Methodology
5.3.1 Data Acquisition and Preprocessing
5.3.2 Proposed Framework
5.3.2.1 EKF for Sensor Fusion
5.3.2.2 PF for Nonlinear Fusion
5.3.2.3 Deep Learning–Based Fusion Using CNNs and Transformers
5.4 Experimentation Results
5.5 Conclusion
References
6. A Hybrid Neurosymbolic Decision-Making Approach with Multimodal Sensor Fusion for Autonomous Vehicles
Devi A., Rose Bindu Joseph P. and Meram Munirathnam
6.1 Introduction
6.2 Related Study
6.3 System Methodology
6.3.1 Perception Module
6.3.2 Hybrid Decision-Making Algorithm for AVs
6.3.3 Trajectory Planning and Execution
6.4 Experimentation Results
6.5 Conclusion
References
7. Reinforcement Learning–Driven Multisensor Fusion for Real-Time Navigation in Intelligent and Opportunistic Vehicular Networks
Mahalakshmi, Suma T., Soya Mathew and Nitya S.
7.1 Introduction
7.2 Related Study
7.3 System Methodology
7.3.1 Perception Module
7.3.2 Proposed Algorithms
7.4 Experimentation Results
7.5 Conclusion
References
8. Hybrid Multimodal Fusion Network (HMFNet) for Enhanced Perception in Autonomous Vehicles
Mahalakshmi, Ranjini K. S., Nidhi S. Vaishnaw and Jesla Joseph
8.1 Introduction
8.2 Related Study
8.3 System Methodology
8.3.1 Dataset Used
8.3.2 Feature Extraction
8.3.3 Proposed HMFNet
8.4 Experimentation Results
8.5 Conclusion
References
9. Fusion-Enhanced Adaptive Learning for Robust Multisensor Integration in Autonomous IoV
A. Radha Krishna, U.V. Ramesh, S. Sathish Kumar and Aimin Li
9.1 Introduction
9.2 Related Study
9.3 System Methodology
9.3.1 Data Acquisition and Sensor Integration
9.3.2 SESW Algorithm
9.3.3 Multiscale Spatiotemporal Fusion Network
9.3.3.1 Feature Extraction Layer
9.3.3.2 Multiscale Fusion Module
9.3.3.3 Decision Refinement Layer
9.3.4 Multitask Output for Perception, Localization, and Path Planning
9.3.5 Final Computation Flow
9.4 Experimentation Results
9.4.1 Localization Accuracy in Simulation
9.4.2 Object Detection and Perception Accuracy
9.4.3 Computational Efficiency and Processing Latency
9.4.4 Decision-Making Latency with V2X Simulation
9.4.5 Path Planning and Collision Avoidance in Simulation
9.5 Conclusion
References
10. Dynamically Reconfigurable Multisensor Fusion for Enhanced Object Detection in Autonomous Vehicles
V. Muthukumaran, M. Sathish Kumar, G. Kumaran, Vidya K.B. and Ahmad Alkhayyat
10.1 Introduction
10.2 Related Study
10.3 System Methodology
10.3.1 Data Acquisition and Preprocessing
10.3.2 Proposed Algorithms
10.4 Experimentation Results
10.5 Conclusion
References
11. AI-Driven Edge Computing for Secure and Efficient Internet of Vehicles (IoV) Communication
Sukumar R. and Saurav Mallik
11.1 Introduction
11.2 Related Study
11.3 System Methodology
11.3.1 Data Collection and Preprocessing
11.3.2 Feature Extraction
11.3.3 Proposed Algorithms
11.4 Experimentation Results
11.5 Conclusion
References
12. Federated Autoencoder-GRU–Based Intrusion Detection System for Secure IoV-Connected Autonomous Vehicles
Pegadapelli Srinivas, Vijey Nathan, Radhika Rajavelu, Suresh Kulandaivelu and Roger Atanga
12.1 Introduction
12.2 Background Study on IoV
12.3 System Methodology
12.3.1 Dataset Description
12.3.2 Data Preprocessing
12.3.3 Proposed Federated Autoencoder-GRU IDS
12.4 Experimental Results
12.5 Conclusion
References
13. Edge-Driven Multimodal Fusion Framework for Real-Time Emotion-Aware Vehicular Networks
Manjula Sanjay Koti, S. Satheesh Kumar, Janani S., Arun A. and Mahmoud Ahmad Al-Khasawneh
13.1 Introduction
13.2 Related Study
13.3 System Methodology
13.3.1 Multimodal Data Acquisition
13.3.2 Signal Preprocessing and Synchronization
13.3.3 Feature Extraction and Fusion
13.3.4 Emotion Recognition Engine
13.3.5 Emotional Readiness for Control Handover
13.4 Experimentation Results
13.5 Conclusion
References
14. Spatiotemporal Attention-Based CNN-BiLSTM Model for Robust Lane and Obstacle Detection in IoV-Enabled Autonomous Driving
Suresh Kulandaivelu, Syied Mazar, Sangeetha N., Sathiyapriya Rajavelu and Anita Garhwal
14.1 Introduction
14.2 Related Study
14.3 System Methodology
14.3.1 Dataset Used and Preprocessing
14.3.2 Network Architecture: Spatiotemporal Attention-Enhanced CNN-BiLSTM
14.3.3 Inference Optimization and Real-Time Deployment
14.4 Experimentation Results
14.5 Conclusion
References
15. Multimodal Vision-LiDAR Transformer Fusion for End-to-End IoV-Based Autonomous Navigation
Mohan Mani, Hariprasath K., C. Vijayakumar, Sathiyapriya Rajavelu and Sarawoot Boonkirdram
15.1 Introduction
15.2 Background Study
15.3 System Methodology
15.3.1 Simulation Environment and Dataset Generation
15.3.2 Multimodal Preprocessing Pipeline
15.3.3 Network Architecture: Transformer-Based Multimodal Fusion
15.4 Experimental Results
15.5 Conclusion
References
Index
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