Autonomous Systems in the Internet of Vehicles.

Format:

Book

Author/Creator:

Balusamy, Balamurugan.

Language:

English

Subjects (All):

Automated vehicles.

Multisensor data fusion.

Physical Description:

1 online resource (325 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

Index

Also of Interest

EULA.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-394-31172-9

1-394-31171-0

9781394311712

OCLC:

1581776205