Upon completing this course, students will achieve the following comprehensive learning outcomes:Foundation of Autonomous Driving: Provide students with a solid understanding of the fundamentals of Autonomous Driving, including its historical context and the core components involved.Technological Background: Explore the underlying technology and principles that drive Autonomous Driving Cars, enabling students to grasp the innovations and advancements in this field.System Components: Familiarize students with the various essential components of Autonomous Driving Cars, giving them insights into the integral parts that make these vehicles autonomous.Autonomy Methods Overview: Provide an overview of diverse methodologies used to achieve autonomy in Autonomous Driving Cars, covering sensor fusion, localization, perception, and decision-making algorithms.Practical Machine Vision and ML: Equip students with practical skills in implementing Machine Vision and Machine Learning algorithms tailored for Autonomous Driving, ensuring hands-on experience.Simulation and Real-Time Testing: Enable students to simulate and rigorously test algorithms using game engines, generating real-time data to assess and refine autonomous systems effectively.

Interactive Lectures and presentationon the topic with examples.Practical Implementation.In-class activities and discussionsAssignmentsOnline Resources on MoodleCollaborative ProjectsCARLA Simulator

1. Theoretical Understanding of Autonomous Vehicles:Gain familiarity with the various levels of Self-Driving Cars.Acquire in-depth knowledge of the underlying technology behind Autonomous Driving Cars.Develop insights into the diverse applications of Autonomous Driving Cars across various industries.Master control, path planning, and tracking techniques relevant to Autonomous Driving.Attain expertise in Image Processing as applied to Self-Driving Cars.2. Proficiency in Deep Learning and Machine Vision for Autonomous Driving Cars:Learn Semantic Segmentation techniques for analyzing scenes effectively.Understand Lane Detection algorithms to ensure safe driving.Gain proficiency in Object Detection methods for identifying obstacles and other vehicles.Master steering control strategies for precise vehicle maneuvering.3. Practical Application using the CARLA Simulator:Apply the acquired knowledge practically by utilizing the CARLA simulator.Implement machine learning techniques and computer vision to simulate and assess Autonomous Driving Cars in a virtual environment.Gain hands-on experience that enhances your ability to develop and evaluate autonomous systems effectively.                                  

Prior Knowledge Expected:Knowledge of Python programming.Background in Deep Learning and Machine Vision.Ability to work with COLAB or Jupyter notebook for implementation.Recommended Courses:Fundamentals of Image ProcessingMachine Learning in Intelligent TransportationPractical Introduction to Neural Networks and Deep LearningArtificial VisionTutorium in Machine Learning, TensorFlow, PyTorch Basics

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Will be uploaded on Moodle

Assignments: HomeworksIn-class activitiesHomework presentationsGroup Presentations: Presentation on selected topics of Autonomous Driving Cars (Week 7 or Week 8)Final Project

Understanding Autonomous CarsUtilizing simple tasks in Computer Vision and Machine learning for Autonomous Driving Cars. Using the CARLA Simulator with implemented topics thoughout the semester

Understanding of the Topic:Proper comprehension of course material.Homework and Final Project Implementation:Effective completion of implementation tasks.Active Learning Throughout the Semester:Active participation and engagement in class activities.