Autonomous Systems in the Internet of Vehicles

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.

• Preface xi1 A Cognitive Edge-Driven Autonomous Learning System for Scalable and Secure IoV Automation 1V. Muthukumaran, S. Satheesh Kumar, Jahnavi S., Rose Bindu Joseph P. and Firoz Khan1.1 Introduction 21.2 Related Study 31.3 System Methodology 71.3.1 Multilayer Edge Computing Framework 71.3.2 Federated Reinforcement Learning Model 101.3.3 Adaptive Dynamic Power Control Algorithm for CEALS 111.4 Experimentation Results 131.5 Conclusion 152 Adaptive Feature Alignment and Fusion for Multisensor Image Integration in the Internet of Vehicles 19Vijay Anand R. and Madala Guru Brahmam2.1 Introduction 202.2 Related Study 222.3 System Methodology 242.3.1 Multisensor Data Acquisition 242.3.2 Preprocessing 252.3.3 Dynamic Feature Alignment in AFAF-Net 252.3.4 Attention-Guided Fusion Method 262.3.5 Real-Time Object Detection 292.4 Experimentation Results 312.5 Conclusion 333 Design of ML-CASF: Multilayer Context-Aware Sensor Fusion for Autonomous Vehicles in the Internet of Vehicles 37Sukumar R. and Sathishkumar V.E.3.1 Introduction 383.2 Related Study 403.3 System Methodology 423.3.1 Sensor Data Acquisition 423.3.2 Preprocessing and Synchronization 423.3.3 Graph Construction for Sensor Data 423.4 Experimentation Results 483.5 Conclusion 524 Adaptive Multimodal Fusion for Robust Autonomous Driving Perception with Attention-Based Learning 55Sangeetha R.4.1 Introduction 564.2 Related Study 594.3 System Methodology 614.3.1 Data Collection and Preprocessing 614.3.2 Feature Extraction 624.3.3 Proposed Methodology 634.4 Experimentation Results 674.4.1 Performance Analysis 684.4.2 Computational Performance Comparison 694.4.3 Impact of Sensor Modalities on Detection Performance 704.5 Conclusion 715 Optimization-Driven Multisensor Fusion Framework for Autonomous Systems in the Internet of Vehicles 75C. Gowdham, A.B. Hajira Be, C. Ashwini, S. Prabu and Zubair Rahaman5.1 Introduction 765.2 Related Study 785.3 System Methodology 825.3.1 Data Acquisition and Preprocessing 825.3.2 Proposed Framework 835.3.2.1 EKF for Sensor Fusion 845.3.2.2 PF for Nonlinear Fusion 855.3.2.3 Deep Learning–Based Fusion Using CNNs and Transformers 855.4 Experimentation Results 865.5 Conclusion 896 A Hybrid Neurosymbolic Decision-Making Approach with Multimodal Sensor Fusion for Autonomous Vehicles 93Devi A., Rose Bindu Joseph P. and Meram Munirathnam6.1 Introduction 946.2 Related Study 966.3 System Methodology 1006.3.1 Perception Module 1006.3.2 Hybrid Decision-Making Algorithm for AVs 1016.3.3 Trajectory Planning and Execution 1036.4 Experimentation Results 1036.5 Conclusion 1057 Reinforcement Learning–Driven Multisensor Fusion for Real-Time Navigation in Intelligent and Opportunistic Vehicular Networks 109Mahalakshmi, Suma T., Soya Mathew and Nitya S.7.1 Introduction 1107.2 Related Study 1127.3 System Methodology 1157.3.1 Perception Module 1157.3.2 Proposed Algorithms 1157.4 Experimentation Results 1207.5 Conclusion 1228 Hybrid Multimodal Fusion Network (HMFNet) for Enhanced Perception in Autonomous Vehicles 127Mahalakshmi, Ranjini K. S., Nidhi S. Vaishnaw and Jesla Joseph8.1 Introduction 1288.2 Related Study 1308.3 System Methodology 1328.3.1 Dataset Used 1328.3.2 Feature Extraction 1338.3.3 Proposed HMFNet 1348.4 Experimentation Results 1388.5 Conclusion 1409 Fusion-Enhanced Adaptive Learning for Robust Multisensor Integration in Autonomous IoV 143A. Radha Krishna, U.V. Ramesh, S. Sathish Kumar and Aimin Li9.1 Introduction 1449.2 Related Study 1489.3 System Methodology 1519.3.1 Data Acquisition and Sensor Integration 1519.3.2 SESW Algorithm 1529.3.3 Multiscale Spatiotemporal Fusion Network 1559.3.3.1 Feature Extraction Layer 1559.3.3.2 Multiscale Fusion Module 1559.3.3.3 Decision Refinement Layer 1569.3.4 Multitask Output for Perception, Localization, and Path Planning 1579.3.5 Final Computation Flow 1579.4 Experimentation Results 1589.4.1 Localization Accuracy in Simulation 1599.4.2 Object Detection and Perception Accuracy 1599.4.3 Computational Efficiency and Processing Latency 1609.4.4 Decision-Making Latency with V2X Simulation 1609.4.5 Path Planning and Collision Avoidance in Simulation 1609.5 Conclusion 16210 Dynamically Reconfigurable Multisensor Fusion for Enhanced Object Detection in Autonomous Vehicles 167V. Muthukumaran, M. Sathish Kumar, G. Kumaran, Vidya K.B. and Ahmad Alkhayyat10.1 Introduction 16810.2 Related Study 17010.3 System Methodology 17310.3.1 Data Acquisition and Preprocessing 17310.3.2 Proposed Algorithms 17410.4 Experimentation Results 18110.5 Conclusion 18311 AI-Driven Edge Computing for Secure and Efficient Internet of Vehicles (IoV) Communication 187Sukumar R. and Saurav Mallik11.1 Introduction 18811.2 Related Study 19111.3 System Methodology 19511.3.1 Data Collection and Preprocessing 19511.3.2 Feature Extraction 19711.3.3 Proposed Algorithms 19711.4 Experimentation Results 20111.5 Conclusion 20712 Federated Autoencoder-GRU–Based Intrusion Detection System for Secure IoV-Connected Autonomous Vehicles 211Pegadapelli Srinivas, Vijey Nathan, Radhika Rajavelu, Suresh Kulandaivelu and Roger Atanga12.1 Introduction 21212.2 Background Study on IoV 21512.3 System Methodology 21812.3.1 Dataset Description 21812.3.2 Data Preprocessing 22012.3.3 Proposed Federated Autoencoder-GRU IDS 22112.4 Experimental Results 22512.5 Conclusion 22913 Edge-Driven Multimodal Fusion Framework for Real-Time Emotion-Aware Vehicular Networks 233Manjula Sanjay Koti, S. Satheesh Kumar, Janani S., Arun A. and Mahmoud Ahmad Al-Khasawneh13.1 Introduction 23413.2 Related Study 23813.3 System Methodology 24313.3.1 Multimodal Data Acquisition 24313.3.2 Signal Preprocessing and Synchronization 24513.3.3 Feature Extraction and Fusion 24613.3.4 Emotion Recognition Engine 24813.3.5 Emotional Readiness for Control Handover 25013.4 Experimentation Results 25313.5 Conclusion 25714 Spatiotemporal Attention-Based CNN-BiLSTM Model for Robust Lane and Obstacle Detection in IoV-Enabled Autonomous Driving 261Suresh Kulandaivelu, Syied Mazar, Sangeetha N., Sathiyapriya Rajavelu and Anita Garhwal14.1 Introduction 26214.2 Related Study 26514.3 System Methodology 26914.3.1 Dataset Used and Preprocessing 26914.3.2 Network Architecture: Spatiotemporal Attention-Enhanced CNN-BiLSTM 27214.3.3 Inference Optimization and Real-Time Deployment 27414.4 Experimentation Results 27514.5 Conclusion 27915 Multimodal Vision-LiDAR Transformer Fusion for End-to-End IoV-Based Autonomous Navigation 283Mohan Mani, Hariprasath K., C. Vijayakumar, Sathiyapriya Rajavelu and Sarawoot Boonkirdram15.1 Introduction 28415.2 Background Study 28715.3 System Methodology 29015.3.1 Simulation Environment and Dataset Generation 29015.3.2 Multimodal Preprocessing Pipeline 29115.3.3 Network Architecture: Transformer-Based Multimodal Fusion 29315.4 Experimental Results 29815.5 Conclusion 302References 303Index 305