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Introduction

The integration of Artificial Intelligence (AI) into autonomous vehicles has revolutionized the automotive industry by providing advanced decision-making capabilities to enhance driver safety, efficiency, and convenience. However, the black-box nature of AI algorithms poses a challenge in understanding how decisions are made within these systems, raising concerns about their reliability and accountability. Explainable AI (XAI) aims to address this issue by providing transparency and interpretability in AI models to enable humans to understand and trust the decisions made by autonomous vehicles.

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

Chapter 2: Literature Review
2.1 Overview of AI in autonomous vehicles
2.2 Importance of explainability in AI
2.3 Existing XAI techniques in autonomous vehicles
2.4 Challenges of implementing XAI in autonomous vehicles
2.5 Impact of XAI on driver trust and acceptance
2.6 Regulatory considerations for XAI in autonomous vehicles
2.7 Ethical implications of XAI in autonomous vehicles
2.8 Case studies of XAI implementation in autonomous vehicles
2.9 Future trends in XAI for autonomous vehicles
2.10 Summary of key findings in the literature review

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection methods
3.3 Data analysis techniques
3.4 XAI model selection criteria
3.5 Implementation of XAI in autonomous vehicles
3.6 Verification and validation process
3.7 Stakeholder engagement and feedback mechanisms
3.8 Ethical considerations in research methodology

Chapter 4: Discussion of Findings
4.1 Overview of XAI implementation in autonomous vehicles
4.2 Evaluation of XAI performance metrics
4.3 Analysis of driver trust and acceptance levels
4.4 Comparison of XAI techniques in autonomous vehicles
4.5 Interpretation of regulatory and ethical implications
4.6 Integration of XAI with existing autonomous vehicle systems
4.7 Recommendations for future research and development
4.8 Implications for the automotive industry
4.9 Limitations of the study
4.10 Conclusion

Chapter 5: Conclusion and Summary
5.1 Summary of key findings
5.2 Contributions to the field of XAI in autonomous vehicles
5.3 Implications for future research and industry applications
5.4 Conclusion

Thesis Overview: Explainable AI for Autonomous Vehicle Decision-Making

The deployment of autonomous vehicles powered by Artificial Intelligence (AI) technologies has introduced a new era in transportation. While AI has improved the efficiency and safety of autonomous vehicles, the lack of transparency in AI decision-making processes has raised concerns about their reliability and accountability. Explainable AI (XAI) offers a solution by providing interpretability and transparency in AI models, enabling users to understand and trust the decisions made by autonomous vehicles. This thesis explores the implementation of XAI in autonomous vehicles and its impact on driver trust, acceptance, and regulatory compliance. Through a thorough literature review, research methodology, and discussion of findings, this thesis aims to contribute to the advancement of XAI in autonomous vehicle decision-making.

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