Autonomous Systems in the Internet of Vehicles
Description
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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Content
- Cover
- Series Page
- Title Page
- Copyright Page
- Contents
- Preface
- Chapter 1 A Cognitive Edge-Driven Autonomous Learning System for Scalable and Secure IoV Automation
- 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
- Chapter 2 Adaptive Feature Alignment and Fusion for Multisensor Image Integration in the Internet of Vehicles
- 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
- Chapter 3 Design of ML-CASF: Multilayer Context-Aware Sensor Fusion for Autonomous Vehicles in the Internet of Vehicles
- 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
- Chapter 4 Adaptive Multimodal Fusion for Robust Autonomous Driving Perception with Attention-Based Learning
- 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
- Chapter 5 Optimization-Driven Multisensor Fusion Framework for Autonomous Systems in the Internet of Vehicles
- 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
- Chapter 6 A Hybrid Neurosymbolic Decision-Making Approach with Multimodal Sensor Fusion for Autonomous Vehicles
- 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
- Chapter 7 Reinforcement Learning-Driven Multisensor Fusion for Real-Time Navigation in Intelligent and Opportunistic Vehicular Networks
- 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
- Chapter 8 Hybrid Multimodal Fusion Network (HMFNet) for Enhanced Perception in Autonomous Vehicles
- 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
- Chapter 9 Fusion-Enhanced Adaptive Learning for Robust Multisensor Integration in Autonomous IoV
- 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
- Chapter 10 Dynamically Reconfigurable Multisensor Fusion for Enhanced Object Detection in Autonomous Vehicles
- 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
- Chapter 11 AI-Driven Edge Computing for Secure and Efficient Internet of Vehicles (IoV) Communication
- 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
- Chapter 12 Federated Autoencoder-GRU-Based Intrusion Detection System for Secure IoV-Connected Autonomous Vehicles
- 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
- Chapter 13 Edge-Driven Multimodal Fusion Framework for Real-Time Emotion-Aware Vehicular Networks
- 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
- Chapter 14 Spatiotemporal Attention-Based CNN-BiLSTM Model for Robust Lane and Obstacle Detection in IoV-Enabled Autonomous Driving
- 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
- Chapter 15 Multimodal Vision-LiDAR Transformer Fusion for End-to-End IoV-Based Autonomous Navigation
- 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
- Also of Interest
- EULA
