Self-Driving Cars: Hands-On with Duckietown
Build and program a scale-model autonomous vehicle. Learn robotics and AI fundamentals through practical, hands-on experience.
Build and program a scale-model autonomous vehicle. Learn robotics and AI fundamentals through practical, hands-on experience.
Dive into the world of autonomous vehicles with this unique, hands-on course using the Duckietown platform. From assembling your own scale-model self-driving car (Duckiebot) to programming it for autonomous navigation, you'll experience the full spectrum of robotics and AI challenges. Learn state-of-the-art approaches in vehicle autonomy, including computer vision, control systems, and machine learning. The course covers both classical architectures and modern AI-based methods, providing a comprehensive understanding of autonomous systems. You'll use industry-standard tools like ROS, Docker, and Python to develop and deploy your solutions. Whether working in simulation or with optional hardware, you'll gain practical skills in robotics software development, sensor integration, and autonomous decision-making. Perfect for aspiring roboticists, AI enthusiasts, and anyone curious about the technology behind self-driving cars.
12,540 already enrolled
Instructors:
Emilio Frazzoli
Jacopo Tani
English
English
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What you'll learn
Program a Duckiebot to navigate autonomously in a model city environment
Implement computer vision techniques for lane following and object detection
Develop control systems for precise robot movement and obstacle avoidance
Apply machine learning algorithms, including neural networks and reinforcement learning
Design and implement state estimation and localization algorithms
Create path planning solutions for navigating complex environments
Skills you'll gain
This course includes:
PreRecorded video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 8 modules in this course
This comprehensive course on self-driving cars using the Duckietown platform covers nine modules over nine weeks. It begins with an introduction to autonomous vehicles and progresses through key topics in robotics and AI. The curriculum includes: robot architecture and modeling, control systems, computer vision, object detection using neural networks, state estimation and localization, path planning, and reinforcement learning. Each module combines theoretical foundations with practical applications, allowing students to implement algorithms on their Duckiebots (either in simulation or with optional hardware). The course emphasizes hands-on learning, with students setting up their development environment, programming their robots, and participating in challenges. By the end, participants will have experience in all aspects of autonomous vehicle development, from low-level control to high-level decision-making algorithms.
Welcome to the course
Module 1
Introduction to self-driving cars
Module 2
Towards autonomy
Module 3
Modeling and Control
Module 4
Object Detection
Module 6
State Estimation and Localization
Module 7
Learning by Reinforcement
Module 8
Fee Structure
Instructors
Emilio Frazzoli
1 Course
Leading Robotics and Autonomous Mobility Expert
Emilio Frazzoli is a full professor of Dynamic Systems and Control at ETH Zürich, where he leads the Institute for Dynamic Systems and Control as well as the Center for Sustainable Future Mobility. His expertise lies primarily in robotics, autonomous vehicles, and smart urban mobility, focusing on planning and control for mobile robotic systems and transportation networks. Before joining ETH Zürich, he was a professor at MIT and served as Chief Scientist at Motional, a leading autonomous vehicle company he co-founded. Frazzoli holds a Laurea in Aeronautical Engineering from Sapienza University of Rome and a PhD in Aeronautics and Astronautics from MIT.
Jacopo Tani
1 Course
Leading Robotics Researcher and Educator
Jacopo Tani is a Senior Researcher at ETH Zürich specializing in robotics and autonomous systems. He holds a Ph.D. in Aerospace Engineering from Rensselaer Polytechnic Institute (RPI) and has previously worked as a Postdoctoral Associate at MIT. At ETH Zürich, Tani combines his background in aerospace engineering with robotics research, contributing significantly to the development of autonomous vehicle platforms like Duckietown, and is actively involved in teaching courses such as "Self-Driving Cars with Duckietown" through ETHx
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