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Introduction

In recent years, the development of autonomous vehicles has gained significant attention due to the potential benefits they offer in terms of safety, efficiency, and convenience. However, one of the key challenges in the deployment of autonomous vehicles is the lack of transparency and interpretability in the decision-making processes of AI systems that control these vehicles. Explainable AI (XAI) aims to address this challenge by providing insights into the inner workings of AI models and algorithms, making the decision-making processes more transparent and understandable to humans.

This thesis focuses on the application of Explainable AI techniques in autonomous vehicle navigation decisions. The goal is to develop a system that not only makes accurate and reliable decisions but also provides explanations for those decisions in a way that is understandable and trustworthy to human users. By improving the transparency and interpretability of AI systems in autonomous vehicles, we can increase the overall safety and acceptance of these vehicles in society.

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 autonomous vehicles
2.2 Explainable AI in autonomous systems
2.3 Challenges in autonomous vehicle decision-making
2.4 XAI techniques and algorithms
2.5 Applications of XAI in autonomous vehicles
2.6 Case studies on XAI in autonomous navigation
2.7 User trust and acceptance of XAI systems
2.8 Ethical and legal considerations in XAI
2.9 Current research gaps in XAI for autonomous vehicles
2.10 Summary of key findings in literature review

Chapter 3: System Design and Methodology
3.1 System architecture for XAI in autonomous vehicles
3.2 Data collection and preprocessing methods
3.3 Feature selection and engineering techniques
3.4 Machine learning models for navigation decisions
3.5 XAI techniques for model interpretation
3.6 Integration of XAI into the decision-making process
3.7 Evaluation metrics for XAI performance
3.8 Testing and validation procedures
3.9 Ethical considerations in system design
3.10 Summary of system design and methodology

Chapter 4: System Implementation
4.1 Implementation of XAI techniques in autonomous vehicle system
4.2 Development of user interface for XAI explanations
4.3 Testing and validation of XAI system
4.4 Performance evaluation of XAI in navigation decisions
4.5 Integration of XAI system into autonomous vehicle platform
4.6 User feedback and system improvements
4.7 Ethical and legal compliance in system implementation
4.8 Summary of system implementation process

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contribution of the study
5.3 Implications for future research
5.4 Limitations and recommendations
5.5 Conclusion

Thesis Overview

The development of autonomous vehicles has revolutionized the automotive industry, with the promise of safer and more efficient transportation systems. However, the lack of transparency in the decision-making processes of AI systems has raised concerns about the reliability and trustworthiness of autonomous vehicles. Explainable AI (XAI) has emerged as a promising solution to this challenge, aiming to provide human-understandable explanations for AI decisions.

This thesis focuses on the application of XAI techniques in autonomous vehicle navigation decisions. The main objective is to develop a system that not only makes accurate and reliable decisions but also provides transparent and interpretable explanations for those decisions. The thesis begins with an introduction to the research topic, followed by a comprehensive literature review on XAI and autonomous vehicles. The system design and methodology chapter details the architecture and implementation of the XAI system, while the system implementation chapter describes the integration of XAI into the autonomous vehicle platform.

In conclusion, this thesis aims to contribute to the advancement of XAI in autonomous vehicles by enhancing the transparency and interpretability of AI decision-making processes. By improving user trust and acceptance of autonomous vehicles, we can accelerate the adoption of this technology and pave the way for a safer and more efficient transportation system.

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