Evaluating Sensor Fusion Techniques for Improved Autonomous Vehicle Navigation in Urban Environments

1. Introduction 1.1 Background and Motivation 1.2 Problem Statement 1.3 Research Objectives 1.4 Structure of the Paper 2. Literature Review 2.1 Autonomous Vehicle Navigation 2.2 Sensor Technologies Overview 2.3 Urban Environments Challenges 2.4 Existing Sensor Fusion Methods 2.5 Evaluation Criteria for Techniques 3. Sensor Technologies 3.1 Lidar and Its Applications 3.2 Radar in Urban Navigation 3.3 Camera Systems Role 3.4 GPS and Localization 3.5 IMU for Stability and Control 4. Sensor Fusion Techniques 4.1 Kalman Filter Approaches 4.2 Particle Filter Applications 4.3 Deep Learning Methods 4.4 Probabilistic Modelling 4.5 Comparative Analysis Approach 5. Evaluation Framework 5.1 Criteria for Assessment 5.2 Simulation and Real-world Trials 5.3 Data Collection Methods 5.4 Evaluation Metrics Defined 5.5 Limitations in Current Methods 6. Case Studies 6.1 Urban Scenario Description 6.2 Implementation of Fusion Techniques 6.3 Performance Results and Analysis 6.4 Comparative Results Discussion 6.5 Lessons Learned from Case Studies 7. Discussion 7.1 Key Findings Discussion 7.2 Implications for Urban Navigation 7.3 Challenges and Future Directions 7.4 Integration with Smart Cities 7.5 Recommendations for Further Research 8. Conclusion 8.1 Summary of Findings 8.2 Contributions to the Field 8.3 Final Thoughts and Outlook