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Introduction

The development of autonomous vehicles has been a significant advancement in the field of transportation, promising to revolutionize the way we travel and commute. However, as autonomous vehicles become more prevalent on our roads, ensuring the safety and efficiency of their route planning algorithms becomes crucial. Explainable Artificial Intelligence (XAI) has emerged as a promising approach to enhance the transparency and interpretability of AI systems, including those used in autonomous vehicles. This thesis aims to explore the application of XAI in autonomous vehicle route planning, with a focus on improving the understanding and trustworthiness of the decision-making process.

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 Two: Literature Review
2.1 Introduction to Autonomous Vehicle Route Planning
2.2 Explainable Artificial Intelligence (XAI)
2.3 Applications of XAI in Autonomous Vehicles
2.4 Challenges in Autonomous Vehicle Route Planning
2.5 Previous Studies on XAI in Autonomous Vehicles
2.6 Interpretable and Transparent Machine Learning Models
2.7 Human Factors in XAI for Autonomous Vehicles
2.8 Ethical Considerations in XAI for Autonomous Vehicles
2.9 Evaluation Metrics for XAI in Autonomous Vehicles
2.10 Future Research Directions in XAI for Autonomous Vehicles

Chapter Three: Research Methodology
3.1 Research Design
3.2 Data Collection
3.3 Model Development
3.4 Evaluation Criteria
3.5 XAI Techniques Implementation
3.6 Case Study Design
3.7 Participant Recruitment
3.8 Data Analysis Techniques

Chapter Four: Discussion of Findings
4.1 Overview of Data Analysis Results
4.2 Comparison of XAI Techniques
4.3 Impact on Route Planning Efficiency
4.4 User Perception and Trust
4.5 Generalization to Real-world Scenarios
4.6 Limitations and Future Research Directions

Chapter Five: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Contributions to the Field
5.3 Practical Implications
5.4 Recommendations for Future Research
5.5 Conclusion

Thesis Overview: Explainable AI for Autonomous Vehicle Route Planning

In recent years, the integration of Artificial Intelligence (AI) in autonomous vehicles has revolutionized the field of transportation. However, the lack of transparency and interpretability of AI algorithms poses significant challenges for the safe and efficient route planning of autonomous vehicles. This thesis aims to address this issue by exploring the application of Explainable Artificial Intelligence (XAI) in autonomous vehicle route planning.

The introduction provides a comprehensive overview of the background, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The literature review discusses the current state of autonomous vehicle route planning, XAI techniques, applications in autonomous vehicles, challenges, previous studies, machine learning models, human factors, ethical considerations, and evaluation metrics.

The research methodology outlines the design, data collection, model development, evaluation criteria, XAI techniques implementation, case study design, participant recruitment, and data analysis techniques. The discussion of findings elaborates on the data analysis results, comparison of XAI techniques, impact on route planning efficiency, user perception and trust, generalization to real-world scenarios, and limitations.

The conclusion and summary provide a summary of key findings, contributions to the field, practical implications, recommendations for future research, and a conclusion. This thesis aims to contribute to the advancement of XAI in autonomous vehicle route planning, ultimately enhancing the safety and efficiency of autonomous vehicles on our roads.

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