CS6143 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING PROJECT EXAMPLE

About This Sample

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. For the assessment of the efficiency of the different approaches to the integration of sensor data to use the results in the development of self-driven cars, the test scenarios were arranged in so-called simulated urban and suburban landscapes. 

Table Of Content

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   
• Decentralized Cooperative Lane Changing Decision-making for Connected Autonomous Vehicles    
• Virtual Fluid Flow Model Based Lane Keeping Integrated with Collision Avoidance Control System Design for Autonomous Vehicles    
• People Tracking by Cooperative Fusion of RADAR and Camera Sensors    
• Autonomous Vehicles and Robust Decision-making in Dynamic Environments    
• 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 and Vision-Based Sensor Integration for Autonomous Navigation    
• Collaborative Autonomous Driving—A Survey of Solution Approaches and Future Challenges    

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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