Analyzing the Impact of Deep Reinforcement Learning on Autonomous Vehicle Navigation Systems

1. Introduction 1.1 Background and Motivation 1.2 Research Objectives 1.3 Structure of the Thesis 2. Fundamentals of Autonomous Vehicles 2.1 Definition and Components 2.2 Current Technological Status 2.3 Challenges in Navigation 3. Overview of Reinforcement Learning 3.1 Basic Concepts and Algorithms 3.2 Differences from Other Learning Methods 3.3 Applications in Various Domains 4. Deep Reinforcement Learning Techniques 4.1 Exploration of Deep Q-Networks 4.2 Policy Gradient Methods 4.3 Actor-Critic Architectures 5. Integration in Autonomous Navigation 5.1 Navigation Strategies 5.2 Sensor Fusion Techniques 5.3 Real-time Decision Making 6. Analyzing Performance Metrics 6.1 KPIs for Autonomous Systems 6.2 Evaluation Frameworks 6.3 Comparative Analysis 7. Case Studies and Experiments 7.1 Real-world Implementations 7.2 Simulation Environment Setup 7.3 Result Analysis and Discussion 8. Conclusion and Future Directions 8.1 Summary of Findings 8.2 Limitations of the Study 8.3 Suggested Future Research Areas