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CS6143 Artificial Intelligence And Machine Learning Project Example

Published: 28 Jan, 2025
Category Dissertation Subject Computer Science
University University Of Limerick Module Title CS6143 Artificial Intelligence And Machine Learning Project
Word Count 50 and 70 pages of main text
Assessment Type Dissertation

ABSTRACT:

This thesis focuses on improving the reliability and safety of autonomous vehicles by using sensor fusion techniques in dynamic high-risk driving conditions. Demonstrating the evaluation of how vehicle environmental perception and behavior are enhanced with LiDAR, radar, and cameras, this study adopts the LiDAR, Camera, and Radar Sensor Integrating (CARLA) Simulator for the assessment........View More

Table Of Contents

ABSTRACT
DECLARATION
ACKNOWLEDGMENTS

Chapter 1: Introduction    

  •  Research Background:    
  •  Research Significance    
  •  Research Aim    
  •  Research Questions    
  •  Research Objectives    
      The specific objectives of this research are to:    
          ->Direction Towards the Research Question    
  •  Challenges and Limitations    
  •  Research Structure    

Chapter 2: Literature Review    

  • Sensor and Sensor Fusion Technology in Autonomous Vehicles  
  • The Rise of Radar for Autonomous Vehicles    
  • Test Scenario Generation for Autonomous Driving Systems with Reinforcement Learning    
  • Multimodal End-to-End Autonomous Driving    
  • Survey of Data Fusion Techniques for Laser
  • Collaborative Autonomous Driving

Chapter 3: Methodology    

  • Overview    
  • Research Design    
  • Participants or Subjects    
  • Materials/Instruments    
      ->CARLA Simulator    
      -> CARLA Simulator Setup:    
      -> Software Tools    
      -> Assessment Tools    
  • Procedure    
      ->Setup Configuration    
      -> Algorithm Implementation    
      -> Simulation Runs    
      -> Data Processing    
      -> Experimental Manipulations    
      ->Performance Evaluation    
      ->Documentation   
  • Data Analysis    
  • Ethical Considerations    
  • Limitations    

Chapter 4: Results    

  • Dataset Overview    
  • Data Preprocessing and Augmentation    
  • Model Training and Evaluation    
  • Visualization of Results    
        ->Genuine Mask Data Against Forecasted Mask    
        -> Quantitative Analysis    
  • Error Analysis    

Chapter 5: Discussion    

  • Discussion    
       ->Overview of Key Findings    
       -> Interpretation of Results    
       ->Comparison with Existing Literature    
       -> Implications for Autonomous Vehicle Technology    

Chapter 6: Conclusion and Recommendations    

  • Key Findings    
  • Practical Recommendations    
  • Limitations of the Study    
  • Recommendations for Future Research    

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