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.
Table of Contents:
Preface xi
1 A Cognitive Edge-Driven Autonomous Learning System for Scalable and Secure IoV Automation 1
V. Muthukumaran, S. Satheesh Kumar, Jahnavi S., Rose Bindu Joseph P. and Firoz Khan
1.1 Introduction 2
1.2 Related Study 3
1.3 System Methodology 7
1.3.1 Multilayer Edge Computing Framework 7
1.3.2 Federated Reinforcement Learning Model 10
1.3.3 Adaptive Dynamic Power Control Algorithm for CEALS 11
1.4 Experimentation Results 13
1.5 Conclusion 15
2 Adaptive Feature Alignment and Fusion for Multisensor Image Integration in the Internet of Vehicles 19
Vijay Anand R. and Madala Guru Brahmam
2.1 Introduction 20
2.2 Related Study 22
2.3 System Methodology 24
2.3.1 Multisensor Data Acquisition 24
2.3.2 Preprocessing 25
2.3.3 Dynamic Feature Alignment in AFAF-Net 25
2.3.4 Attention-Guided Fusion Method 26
2.3.5 Real-Time Object Detection 29
2.4 Experimentation Results 31
2.5 Conclusion 33
3 Design of ML-CASF: Multilayer Context-Aware Sensor Fusion for Autonomous Vehicles in the Internet of Vehicles 37
Sukumar R. and Sathishkumar V.E.
3.1 Introduction 38
3.2 Related Study 40
3.3 System Methodology 42
3.3.1 Sensor Data Acquisition 42
3.3.2 Preprocessing and Synchronization 42
3.3.3 Graph Construction for Sensor Data 42
3.4 Experimentation Results 48
3.5 Conclusion 52
4 Adaptive Multimodal Fusion for Robust Autonomous Driving Perception with Attention-Based Learning 55
Sangeetha R.
4.1 Introduction 56
4.2 Related Study 59
4.3 System Methodology 61
4.3.1 Data Collection and Preprocessing 61
4.3.2 Feature Extraction 62
4.3.3 Proposed Methodology 63
4.4 Experimentation Results 67
4.4.1 Performance Analysis 68
4.4.2 Computational Performance Comparison 69
4.4.3 Impact of Sensor Modalities on Detection Performance 70
4.5 Conclusion 71
5 Optimization-Driven Multisensor Fusion Framework for Autonomous Systems in the Internet of Vehicles 75
C. Gowdham, A.B. Hajira Be, C. Ashwini, S. Prabu and Zubair Rahaman
5.1 Introduction 76
5.2 Related Study 78
5.3 System Methodology 82
5.3.1 Data Acquisition and Preprocessing 82
5.3.2 Proposed Framework 83
5.3.2.1 EKF for Sensor Fusion 84
5.3.2.2 PF for Nonlinear Fusion 85
5.3.2.3 Deep Learning–Based Fusion Using CNNs and Transformers 85
5.4 Experimentation Results 86
5.5 Conclusion 89
6 A Hybrid Neurosymbolic Decision-Making Approach with Multimodal Sensor Fusion for Autonomous Vehicles 93
Devi A., Rose Bindu Joseph P. and Meram Munirathnam
6.1 Introduction 94
6.2 Related Study 96
6.3 System Methodology 100
6.3.1 Perception Module 100
6.3.2 Hybrid Decision-Making Algorithm for AVs 101
6.3.3 Trajectory Planning and Execution 103
6.4 Experimentation Results 103
6.5 Conclusion 105
7 Reinforcement Learning–Driven Multisensor Fusion for Real-Time Navigation in Intelligent and Opportunistic Vehicular Networks 109
Mahalakshmi, Suma T., Soya Mathew and Nitya S.
7.1 Introduction 110
7.2 Related Study 112
7.3 System Methodology 115
7.3.1 Perception Module 115
7.3.2 Proposed Algorithms 115
7.4 Experimentation Results 120
7.5 Conclusion 122
8 Hybrid Multimodal Fusion Network (HMFNet) for Enhanced Perception in Autonomous Vehicles 127
Mahalakshmi, Ranjini K. S., Nidhi S. Vaishnaw and Jesla Joseph
8.1 Introduction 128
8.2 Related Study 130
8.3 System Methodology 132
8.3.1 Dataset Used 132
8.3.2 Feature Extraction 133
8.3.3 Proposed HMFNet 134
8.4 Experimentation Results 138
8.5 Conclusion 140
9 Fusion-Enhanced Adaptive Learning for Robust Multisensor Integration in Autonomous IoV 143
A. Radha Krishna, U.V. Ramesh, S. Sathish Kumar and Aimin Li
9.1 Introduction 144
9.2 Related Study 148
9.3 System Methodology 151
9.3.1 Data Acquisition and Sensor Integration 151
9.3.2 SESW Algorithm 152
9.3.3 Multiscale Spatiotemporal Fusion Network 155
9.3.3.1 Feature Extraction Layer 155
9.3.3.2 Multiscale Fusion Module 155
9.3.3.3 Decision Refinement Layer 156
9.3.4 Multitask Output for Perception, Localization, and Path Planning 157
9.3.5 Final Computation Flow 157
9.4 Experimentation Results 158
9.4.1 Localization Accuracy in Simulation 159
9.4.2 Object Detection and Perception Accuracy 159
9.4.3 Computational Efficiency and Processing Latency 160
9.4.4 Decision-Making Latency with V2X Simulation 160
9.4.5 Path Planning and Collision Avoidance in Simulation 160
9.5 Conclusion 162
10 Dynamically Reconfigurable Multisensor Fusion for Enhanced Object Detection in Autonomous Vehicles 167
V. Muthukumaran, M. Sathish Kumar, G. Kumaran, Vidya K.B. and Ahmad Alkhayyat
10.1 Introduction 168
10.2 Related Study 170
10.3 System Methodology 173
10.3.1 Data Acquisition and Preprocessing 173
10.3.2 Proposed Algorithms 174
10.4 Experimentation Results 181
10.5 Conclusion 183
11 AI-Driven Edge Computing for Secure and Efficient Internet of Vehicles (IoV) Communication 187
Sukumar R. and Saurav Mallik
11.1 Introduction 188
11.2 Related Study 191
11.3 System Methodology 195
11.3.1 Data Collection and Preprocessing 195
11.3.2 Feature Extraction 197
11.3.3 Proposed Algorithms 197
11.4 Experimentation Results 201
11.5 Conclusion 207
12 Federated Autoencoder-GRU–Based Intrusion Detection System for Secure IoV-Connected Autonomous Vehicles 211
Pegadapelli Srinivas, Vijey Nathan, Radhika Rajavelu, Suresh Kulandaivelu and Roger Atanga
12.1 Introduction 212
12.2 Background Study on IoV 215
12.3 System Methodology 218
12.3.1 Dataset Description 218
12.3.2 Data Preprocessing 220
12.3.3 Proposed Federated Autoencoder-GRU IDS 221
12.4 Experimental Results 225
12.5 Conclusion 229
13 Edge-Driven Multimodal Fusion Framework for Real-Time Emotion-Aware Vehicular Networks 233
Manjula Sanjay Koti, S. Satheesh Kumar, Janani S., Arun A. and Mahmoud Ahmad Al-Khasawneh
13.1 Introduction 234
13.2 Related Study 238
13.3 System Methodology 243
13.3.1 Multimodal Data Acquisition 243
13.3.2 Signal Preprocessing and Synchronization 245
13.3.3 Feature Extraction and Fusion 246
13.3.4 Emotion Recognition Engine 248
13.3.5 Emotional Readiness for Control Handover 250
13.4 Experimentation Results 253
13.5 Conclusion 257
14 Spatiotemporal Attention-Based CNN-BiLSTM Model for Robust Lane and Obstacle Detection in IoV-Enabled Autonomous Driving 261
Suresh Kulandaivelu, Syied Mazar, Sangeetha N., Sathiyapriya Rajavelu and Anita Garhwal
14.1 Introduction 262
14.2 Related Study 265
14.3 System Methodology 269
14.3.1 Dataset Used and Preprocessing 269
14.3.2 Network Architecture: Spatiotemporal Attention-Enhanced CNN-BiLSTM 272
14.3.3 Inference Optimization and Real-Time Deployment 274
14.4 Experimentation Results 275
14.5 Conclusion 279
15 Multimodal Vision-LiDAR Transformer Fusion for End-to-End IoV-Based Autonomous Navigation 283
Mohan Mani, Hariprasath K., C. Vijayakumar, Sathiyapriya Rajavelu and Sarawoot Boonkirdram
15.1 Introduction 284
15.2 Background Study 287
15.3 System Methodology 290
15.3.1 Simulation Environment and Dataset Generation 290
15.3.2 Multimodal Preprocessing Pipeline 291
15.3.3 Network Architecture: Transformer-Based Multimodal Fusion 293
15.4 Experimental Results 298
15.5 Conclusion 302
References 303
Index 305
