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Introduction

The advancement of computer vision technology has opened up new opportunities in various industries, including the automotive sector. Autonomous vehicles, also known as self-driving cars, are one of the areas where computer vision is being applied to enhance safety, efficiency, and overall performance. This thesis explores the application of computer vision in autonomous vehicles and its impact on the future of transportation.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of Computer Vision
2.2 History of Autonomous Vehicles
2.3 Applications of Computer Vision in Autonomous Vehicles
2.4 Challenges in Implementing Computer Vision in Autonomous Vehicles
2.5 Performance Metrics for Autonomous Vehicles
2.6 Current Trends in Autonomous Vehicles
2.7 Legal and Ethical Implications of Autonomous Vehicles
2.8 Comparison of Different Computer Vision Algorithms
2.9 Case Studies of Companies Implementing Computer Vision in Autonomous Vehicles
2.10 Future Directions in the Field of Autonomous Vehicles

Chapter 3: System Design and Methodology
3.1 System Architecture
3.2 Data Collection and Preprocessing
3.3 Object Detection and Recognition
3.4 Lane Detection and Tracking
3.5 Sensor Fusion
3.6 Decision Making and Control
3.7 Testing and Evaluation
3.8 Performance Optimization

Chapter 4: System Implementation
4.1 Hardware Requirements
4.2 Software Development
4.3 Integration of Computer Vision Algorithms
4.4 Training and Testing Data
4.5 Validation and Verification
4.6 Real-world Testing
4.7 Performance Evaluation
4.8 System Maintenance and Updates

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Future Research Directions
5.4 Conclusion

Thesis Overview:

The use of computer vision in autonomous vehicles has gained significant attention in recent years due to its potential to revolutionize the transportation industry. This thesis aims to explore the application of computer vision technology in autonomous vehicles and its impact on the safety, efficiency, and overall performance of self-driving cars.

Chapter 1 provides an introduction to the topic, including the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Definitions of key terms related to the field of autonomous vehicles and computer vision are also provided to clarify the concepts discussed in the subsequent chapters.

Chapter 2 presents a comprehensive literature review on computer vision, autonomous vehicles, applications of computer vision in autonomous vehicles, challenges, performance metrics, trends, legal implications, and comparison of different algorithms. Case studies of companies implementing computer vision in autonomous vehicles are also analyzed.

In Chapter 3, the system design and methodology for implementing computer vision in autonomous vehicles are discussed in detail. Topics covered include system architecture, data collection, preprocessing, object detection, lane detection, sensor fusion, decision making, control, testing, evaluation, and performance optimization.

Chapter 4 focuses on the system implementation process, including hardware requirements, software development, integration of computer vision algorithms, training, validation, testing, performance evaluation, and maintenance. Practical aspects of implementing computer vision in autonomous vehicles are explored to provide a hands-on understanding of the technology.

Chapter 5 concludes the thesis by summarizing the findings, discussing the contributions to the field, suggesting future research directions, and concluding the study. The key takeaways from the research are highlighted, and recommendations for further exploration in the field of applying computer vision to autonomous vehicles are provided.

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