The F1TENTH Program and Foundation Promote STEM education in the following ways:

  1. Develops teaching and training materials for university and K-12 courses on robotics, self-driving vehicles and on the foundations of autonomous systems. This includes the fundamentals of robot perception, computer-vision, robot localization and mapping, motion planning and safe control. These are currently being used and taught by over 60 universities that are part of the F1Tenth Community. This includes universities such as University of California at San Diego, Clemson University, Oregon State University, University of Virginia, Lehigh University, University of Pennsylvania and many more. For details see
  2. Organizes education events such as workshops, tutorials, seminars and panels on building safe autonomous vehicles. These events are organized at IEEE and ACM accredited engineering and computing conferences.
  3. Organizes community events such as international autonomous racing. We have hosted over 11 international autonomous racing competitions across New York, Pittsburgh, Portugal, South Korea, Italy, Canada, Vienna, etc. For details see
  4. We develop and share open-source software and hardware designs for anyone to freely make autonomous robotics platforms which can sense, plan and act. For details see

Here is what current F1TENTH users say:

I took F 1/10th Autonomous Robots course at UT Austin and I would highly recommend taking this course for anyone who wants to foray into the field of autonomous vehicles. This course was successful in covering all the basics for estimation, mapping, and obstacle avoidance. Deploying these algorithms on actual cars is also fun with a great learning curve and is something every roboticist should experience.

I took the F1Tenth autonomous racing course during my masters at UPenn and it was no doubt one of the best courses there. It was a very hands-on course and I got to implement various motion planning, perception, and control algorithms on the F1/10 car. Understanding the theory, coding up the algorithms, and debugging issues that arose during hardware implementation taught me invaluable lessons and made me ready for the challenges that I would face in real-world scenarios. This experience solidified my decision to delve deeper into the field and pursue a Ph.D. in Autonomous Systems at Washington University at St. Louis.

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