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2Month/20 Hour Price:125,000
120,000
Self-Driving Cars with Duckietown
This course provides a hands-on introduction to the key concepts and technologies behind self-driving cars using the Duckietown platform. Learners will explore autonomous navigation, computer vision, control systems, and machine learning, all within the context of Duckietown's miniaturized self-driving car environment. Through engaging labs and projects, participants will gain practical experience in programming autonomous robots to drive, navigate, and avoid obstacles in a simulated urban environment.
Course Objectives:
Understand the foundational principles of autonomous driving
Develop and implement algorithms for vehicle localization, perception, and control
Work with computer vision techniques for road detection, object tracking, and navigation
Apply control strategies to manage autonomous vehicle motion and trajectory planning
Build a mini self-driving car using the Duckietown platform and program it to navigate a cityscape
Develop and implement algorithms for vehicle localization, perception, and control
Work with computer vision techniques for road detection, object tracking, and navigation
Apply control strategies to manage autonomous vehicle motion and trajectory planning
Build a mini self-driving car using the Duckietown platform and program it to navigate a cityscape
Course Content:
Module 1: Introduction to Self-Driving Cars and Duckietown
Overview of Autonomous Vehicles:History and future of self-driving cars
The five levels of autonomy in self-driving systems
The five levels of autonomy in self-driving systems
Duckietown Platform Introduction:
Overview of the Duckietown educational platform
Hardware setup: Duckiebot assembly and components (camera, motors, sensors)
Software overview: Duckietown operating system, ROS (Robot Operating System) basics
Hardware setup: Duckiebot assembly and components (camera, motors, sensors)
Software overview: Duckietown operating system, ROS (Robot Operating System) basics
Module 2: Robot Kinematics and Motion Control
Introduction to Robot Kinematics:
Differential drive kinematics and odometryRobot motion and steering
Basic Motion Control:
Proportional-integral-derivative (PID) control for autonomous motion
Controlling speed and direction of Duckiebots
Controlling speed and direction of Duckiebots
Module 3: Computer Vision for Autonomous Driving
Image Processing Fundamentals:Introduction to computer vision and OpenCV
Pre-processing techniques: Thresholding, edge detection, and contour detection
Pre-processing techniques: Thresholding, edge detection, and contour detection
Road and Lane Detection:
How to detect road lanes using computer vision
Applying the Hough Transform for line detection
Identifying lane markings and keeping the Duckiebot on track
Applying the Hough Transform for line detection
Identifying lane markings and keeping the Duckiebot on track
Object Detection and Tracking:
Techniques for detecting and tracking objects (e.g., other vehicles, pedestrians)
Using bounding boxes and color segmentation for obstacle avoidance
Using bounding boxes and color segmentation for obstacle avoidance
Module 4: Localization and Mapping
Introduction to Localization:Concept of localization and why it is important for self-driving cars
Understanding sensor fusion techniques (e.g., combining camera data with odometry)
Understanding sensor fusion techniques (e.g., combining camera data with odometry)
Simultaneous Localization and Mapping (SLAM):Basics of SLAM for building maps while navigating the environment
How Duckiebot uses SLAM to create and update maps of the Duckietown environment
Module 5: Path Planning and Obstacle Avoidance (10 Hours)
Path Planning Algorithms:Overview of different path planning algorithms (A*, Dijkstra, RRT)
How to plan and execute paths for autonomous navigation in a dynamic environment
How to plan and execute paths for autonomous navigation in a dynamic environment
Obstacle Avoidance Strategies:
Real-time obstacle detection and avoidance techniques
Reactive control methods: Handling dynamic obstacles such as moving cars or pedestrians
Reactive control methods: Handling dynamic obstacles such as moving cars or pedestrians
Module 6: Deep Learning for Autonomous Driving (5 Hours)
Introduction to Deep Learning for Self-Driving Cars:Overview of neural networks and their application in autonomous vehicles
Using Convolutional Neural Networks (CNNs) for object recognition and lane detection
Using Convolutional Neural Networks (CNNs) for object recognition and lane detection
Training and Evaluating Models:
Collecting training data using Duckiebot's camera
Training models for visual navigation and obstacle detection
Training models for visual navigation and obstacle detection
Module 7: Vehicle-to-Infrastructure (V2I) Communication and Traffic Management
Understanding V2I Communication:
Introduction to vehicle-to-infrastructure communication
How Duckiebot communicates with traffic lights, stop signs, and other infrastructure
How Duckiebot communicates with traffic lights, stop signs, and other infrastructure
Traffic Light Detection and Response:
Detecting and responding to traffic lights and stop signs using computer vision
Implementing traffic management rules in Duckietown (e.g., stop at red lights)
Implementing traffic management rules in Duckietown (e.g., stop at red lights)
Program Duckiebot to recognize traffic signals and manage traffic interactions
Course Prerequisites:
Basic programming knowledge in Python or a similar languageFamiliarity with Linux and command-line interfaces is helpful but not required
Basic understanding of robotics, sensors, and control systems is beneficial but not mandatory
Career Path After Completion:
Upon completing this course, participants can pursue roles in fields such as:
Autonomous Vehicle Engineer: Work on developing self-driving technology for real-world applications.Robotics Engineer: Design and program robots with autonomous capabilities.
Computer Vision Engineer: Specialize in image processing and visual navigation for autonomous systems.
Control Systems Engineer: Develop advanced control systems for autonomous vehicle navigation.
AI and Machine Learning Engineer: Focus on integrating AI models for object detection, path planning, and obstacle avoidance in autonomous vehicles.
International Student Fees; USD 525
Job Interview Preparation (Soft Skills Questions & Answers)
Tough Open-Ended Job Interview QuestionsWhat to Wear for Best Job Interview Attire
Job Interview Question- What are You Passionate About?
How to Prepare for a Job Promotion Interview
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