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F1Tenth - UC Santa Barbara


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 the foundations of autonomous driving, 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 the fundamentals of autonomous driving in the field of perception, planning and control and how to program them. 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 15 week course we will provide you the most comprehensive learning experience in the field of autonomous driving and teach you about the theory and programming of methods and algorithms in the field of perception, mapping, localization, trajectory planning and control. As an add on we will have a discussion about the moral implications of autonomous systems.

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
  • Different methods for calculating the raceline for a given track
  • Different methods for detection an object
  • The deployment of software on real hardware
  • GPU acceleration for Machine Learning algorithms
  • Differences between SiL and HiL development
  • Head-to-Head racing
  • Differences between SiL and HiL development
  • Analytics skills to recognize and reason about situations with moral content


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. Joao Hespanha

João P. Hespanha was born in Coimbra, Portugal, in 1968. He received the Licenciatura in electrical and computer engineering from the Instituto Superior Técnico, Lisbon, Portugal in 1991 and the Ph.D. degree in electrical engineering and applied science from Yale University, New Haven, Connecticut in 1998. From 1999 to 2001, he was Assistant Professor at the University of Southern California, Los Angeles. He moved to the University of California, Santa Barbara in 2002, where he currently holds a Professor position with the Department of Electrical and Computer Engineering. Dr. Hespanha is the recipient of the Yale University’s Henry Prentiss Becton Graduate Prize for exceptional achievement in research in Engineering and Applied Science, a National Science Foundation CAREER Award, the 2005 best paper award at the 2nd Int. Conf. on Intelligent Sensing and Information Processing, the 2005 Automatica Theory/Methodology best paper prize, the 2006 George S. Axelby Outstanding Paper Award, and the 2009 Ruberti Young Researcher Prize. Dr. Hespanha is a Fellow of the International Federation of Automatic Control (IFAC) and of the IEEE. He was an IEEE distinguished lecturer from 2007 to 2013. His current research interests include hybrid and switched systems; multi-agent control systems; game theory; optimization; distributed control over communication networks (also known as networked control systems); the use of vision in feedback control; stochastic modeling in biology; and network security.

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