A virtual, autonomous racing challenge launching this week will enable aspiring drivers to head to the track without leaving their computer.
The Learn-to-Race Autonomous Racing Virtual Challenge started Monday, Dec. 6. Competitors use the Learn-to-Race environment to teach an artificially intelligent agent how to race. The challenge is coupled with a workshop on Safe Learning for Autonomous Driving, which is accepting research paper submissions.
"We want people to use Learn-to-Race, make improvements to the environment, push it to the limit and create an agent that could run on a track," said James Herman, a CMU alumnus who wrote the Learn-to-Race framework and is part of the team launching the challenge. "Hopefully, people will have fun with it and come up with creative ideas."
Herman graduated this past May from the Master of Computational Data Science (MCDS) program in the Language Technologies Institute (LTI) at CMU's School of Computer Science. He now works in New York as a machine learning engineer at Intellimize.
What drew Herman to autonomous racing had nothing to do with cars or going fast. During Herman's first month at CMU, Anirudh Koul, also an MCDS alumnus, gave a presentation that captured his attention, and Herman decided he wanted to work with Koul. That next semester, Koul announced he was sponsoring a capstone project with Siddha Ganju, another MCDS alumna, to work on autonomous racing. Herman was interested.
That capstone project led to Herman proposing that CMU enter the Roborace Challenge, a global autonomous racing competition. CMU's team became the first from North America to enter, placing second and third in events during its first season.
