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F1Tenth - UC Irvine


About This Course

Whether it's well-known car manufacturer like Tesla or new technology companies like Google: many different companies are working on the development of autonomous vehicles. This emerging field provides a lot of potential and if you are interested in learning how to drive with an autonomous vehicle on a given map, then this course is for you.

But don't be worried about that this is another theory-only lecture series: In this course we will work hands-on and lab centered for everyone who is interested in the field of artificial intelligence, motion planning, control theory, and applied machine learning. From the very beginning we will introduce you to both a simulation environment and real hardware: Our 1/10th-scale autonomous race car.

We will not only teach you about the theory, you will also learn how to program the algorithms. We will also show you how to bring your autonomous driving algorithms on the real F1TENTH Hardware. By focusing on the racing environment you will also learn how to develop algorithms that operate on the edge of vehicle dynamics: High accelerations, high velocities and high computation frequencies.

In the 10 week course we will teach you about the fundamentals in autonomous driving and will focus on efficient racing on a given map. We will show you different methods for creating a map and localizing the car afterwards on that map. In addition we will try out different path planners to make the car more agile on the racetrack

What you'll learn

After attending the course you will have a comprehensive overview of the perception, planning and control methods used in autonomous driving. You will be able to select the appropriate method and algorithm for various autonomous driving problems and then implement it with appropriate code. In addition you are able to program the F1TENTH Autonomous Race car to drive fast and safe around a racetrack. In addition you will learn:

  • Introduction to the ROS framework
  • Refresh your control theory knowledge
  • Control theory and control application
  • Introduction to an Simulator for autonomous driving
  • Different techniques for localization of the car
  • Different techniques for the fast and secure path planning
  • The deployment of software on real hardware
  • Differences between SiL and HiL development
  • Head-to-Head racing


This course is an integration of the concepts above and is not intended to be a beginner course in any of the above subjects. As of right now, the contents on this website are best suited for the graduate-level if not at the very least an undergraduate senior-level course.

Course Staff

Course Staff Image #1

Prof. Dr. Yasser Shoukry

I am an assistant professor in the Department of Electrical Engineering and Computer Science at the University of California, Irvine where I direct the Resilient Cyber-Physical Systems Lab. Before joining UCI, I spent two years as an assistant professor in the Department of Electrical and Computer Engineering at the University of Maryland, College Park. Before that, I was a joint postdoctoral associate at the University of California, Berkeley, University of California, Los Angeles, and the University of Pennsylvania working with Prof. George J. Pappas, Prof. Sanjit A. Seshia, and Prof. Paulo Tabuada. I received my Ph.D. from the Electrical Engineering Department at the University of California, Los Angeles in 2015 under the supervision of Prof. Paulo Tabuada and Prof. Mani Srivastava. My research goal is to develop algorithms and tools to reason about the resilience, security, and privacy of Artificial Intelligence (AI) controlled Cyber-Physical Systems and Internet-of-Things (IoT), in general, and robotic systems, in particular, providing a scientific basis to understand their fundamental properties and guide their design. My work spans both theoretical and experimental aspects of CPS and draws on tools from formal methods, embedded systems, control theory, and machine learning.

Frequently Asked Questions

What web browser should I use?

The Open edX platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. See our list of supported browsers for the most up-to-date information.

Do i need to buy and built the F1Tenth Hardware?

The car is an additional add-on and provides you the important insight in the deployment of the software on real hardware.

  1. Course Number

  2. Classes Start

  3. Classes End

  4. Estimated Effort

    20 hours per Week