AI in Autonomous Vehicles

Aim

Program Objectives

Program Structure

1. Introduction to Autonomous Vehicles and AI
   • Overview of Autonomous Vehicle Levels (SAE Levels 0-5)
   • Role of AI in Autonomous Driving
   • Use Cases and Applications (Self-Driving Cars, Drones, Delivery Robots)
2. Sensors and Perception in Autonomous Vehicles
   • Types of Sensors: LIDAR, Radar, Cameras, Ultrasonics
   • Sensor Fusion for Accurate Environment Perception
   • AI for Perception: Object Detection, Lane Detection, and Segmentation
3. Computer Vision in Autonomous Driving
   • Basics of Computer Vision for Autonomous Vehicles
   • Deep Learning Techniques for Object Detection (YOLO, SSD)
   • Real-Time Image Processing for Vehicle Cameras
4. Localization and Mapping
   • Simultaneous Localization and Mapping (SLAM)
   • GPS, IMU, and Dead Reckoning for Vehicle Localization
   • AI Techniques for Accurate Mapping (Visual SLAM, Particle Filters)
5. Path Planning and Motion Control
   • Path Planning Algorithms (Dijkstra, A*, RRT)
   • AI-Based Path Planning: Reinforcement Learning for Autonomous Navigation
   • Motion Control Techniques (PID, MPC) for Smooth Driving
6. Deep Learning for Autonomous Vehicles
   • Neural Networks for Perception and Decision-Making
   • Convolutional Neural Networks (CNNs) for Image Processing
   • Recurrent Neural Networks (RNNs) for Sequential Data (Sensor Data)
7. Reinforcement Learning in Autonomous Driving
   • Fundamentals of Reinforcement Learning (Q-Learning, Policy Gradient)
   • Applying RL to Decision-Making in Autonomous Vehicles
   • Case Studies: Self-Learning Driving Agents
8. Vehicle-to-Everything (V2X) Communication
   • Communication between Vehicles (V2V), Infrastructure (V2I), and Networks (V2N)
   • AI for Optimizing V2X Communication
   • Safety and Traffic Management through AI-Driven V2X Systems
9. Autonomous Vehicle Software Architecture
   • Overview of Autonomous Driving Stacks (Apollo, Autoware)
   • ROS (Robot Operating System) for Autonomous Vehicles
   • AI Integration into Autonomous Vehicle Software Pipelines
10. Simulation and Testing of Autonomous Vehicles
    • Virtual Environments for Testing Autonomous Vehicles (CARLA, AirSim)
    • AI for Simulation-Based Testing and Validation
    • Reinforcement Learning for Simulated Driving Scenarios
11. Safety, Ethics, and Regulations in Autonomous Driving
    • AI for Safety-Critical Systems in Autonomous Vehicles
    • Ethical Considerations in AI-Driven Decision Making
    • Legal and Regulatory Framework for Autonomous Vehicles

Who Should Enrol?

Program Outcomes

Fee Structure

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