Autonomous Systems in the Internet of Vehicles

Balamurugan Balusamy, Sandeep Kumar Mathivanan, Prabhu Jayagopal, S. K. B. Sangeetha, Ali Kashif Bashir

• Publisher: Wiley-Scrivener
• Year: 2026
• Print ISBN:
  9781394311699
• Edition: 1
• eBook ISBN:
  9781394311705
• Topic: Computer Science
• Subtopic: Computer Engineering
• Index: Computer Science

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

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