Formula Student Driverless

The Formula Student Driverless team is CAR’s largest student race team, with 40+ technical members. It was created in September 2021 when several Carnegie Mellon Racing students began exploring autonomously retrofitting an electrical formula student race car. The team is currently preparing for an exhibition race in June 2023!

Advisors

Ding Zhao

Assistant Professor

Stanford University ‘00

Ding Zhao is an assistant professor of Mechanical Engineering. He is also associated with the Robotics Institute and the Machine Learning Department at the School of Computer Science and the Wilton E. Scott Institute for Energy Innovation. Directing the Safe AI Laboratory, Zhao aims to develop verifiable, affordable, and good-for-all learning for robotics in the face of the uncertain, dynamic, time-varying, multiple agents, and possibly human-involved environment by bridging statistics and cybernetics.

David Held

Assistant Professor

Stanford University ‘00

David Held is an assistant professor at Carnegie Mellon University in the Robotics Institute and is the director of the RPAD (Robots Perceiving And Doing) Lab. His research focuses on perceptual robot learning and he has applied these ideas to robot manipulation and autonomous driving. Before coming to CMU, David was a post-doctoral researcher at U.C. Berkeley, and he completed his Ph.D. in Computer Science at Stanford University. David is a recipient of the Google Faculty Research Award in 2017 and the NSF CAREER Award in 2021.

Raj Rajkumar

Director

Stanford University ‘00

Prof. Raj Rajkumar is a George Westinghouse Professor in the Department of Electrical and Computer Engineering at Carnegie Mellon University. He also hold additional roles: Director, Metro21: CMU's Campus-Wide Initiative on Smart Cities. Director, USDOT Mobility21 National University Transportation Center.Director, USDOT T-SET National University Transportation Center.

Team Leads

Alex Yoon

Working on the vehicle integration team whose focus is on designing autonomous systems, both by modifying the existing FSAE vehicle, and by engineering new systems to integrate autonomous software into the vehicle.

Ravi Patel

Working on the vehicle integration team whose focus is on designing autonomous systems, both by modifying the existing FSAE vehicle, and by engineering new systems to integrate autonomous software into the vehicle.

Andrew Chong

Working on the path planning pipeline which takes the categorized data from perceptions, and develops a racing line for the car to follow. This is achieved by developing and applying algorithms that calculate the safest and fastest way through the racetrack.

Siddhant Sapra

Working on the path planning pipeline which takes the categorized data from perceptions, and develops a racing line for the car to follow. This is achieved by developing and applying algorithms that calculate the safest and fastest way through the racetrack.

Thomas Liao

Working on the controls pipeline which takes the route provided by path planning, and calculates what inputs are needed to allow the car to follow that line. To do this, the team develops models of what the car is capable of, and applies these inputs to the modified vehicle.

Grace Tang

Working on the software architecture team which focuses on developing the communication protocols between the different parts of our autonomous pipeline.

Deep Patel

Working on the perception pipeline which takes the data received from the LiDAR and Stereo Camera array, and uses it to map out the racetrack. Utilizing various point clustering and depth estimation algorithms, which allow the car to understand what its environment looks like.

Ankit Khandelwal

Working on the perception pipeline which takes the data received from the LiDAR and Stereo Camera array, and uses it to map out the racetrack. Utilizing various point clustering and depth estimation algorithms, which allow the car to understand what its environment looks like.

Technology

Perception

Use state-of-the-art perception metrics and advanced machine learning algorithms to find depth estimation via Stereo Frame and LiDAR sensing. Path Planning: Dynamic data collection for geolocation and mapping to optimize the race line even before starting the lap.

Path Planning

Dynamic data collection for geolocation and mapping to optimize the race line even before starting the lap.

Software

Advanced embedded software programming to create a data frame of state fields, optimal trajectory, diagnostics log, error handling, and read, write and configure various real-time race tasks.

Controls

Controls systems to determine actuator commands to push the racecar along itsoptimal trajectory.

Vehicle

All of the aforementioned technologies, software design, and mechanical innovations are integrated seamlessly to create a highly competitive race car design that encapsulates CMU's expertise in robotics, advanced computing, and mechanical engineering.