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Autonomous Systems in the Internet of Vehicles.

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...

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Bibliographic Details
Main Author: Balusamy, Balamurugan
Format: eBook
Language:English
Published: Newark : John Wiley & Sons, Incorporated, 2026.
Edition:1st ed.
Subjects:
Automated vehicles.
Multisensor data fusion.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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.