A team of researchers at Carnegie Mellon University (CMU) are bringing us one step closer to achieving self-driving all-terrain vehicles (ATVs). The team rode an ATV through various different environments including tall grass, loose gravel, and mud to gather data on how the ATV interacted with these types of off-road environments.
The ATV was driven aggressively at speeds up to 30 miles per hour. It slid through turns, went up and down hills, and got stuck in the mud while gathering important data like video, the speed of each wheel, and the suspension shock travel from seven types of sensors.
After collecting all of the data, it was compiled into a dataset called TartanDrive. It includes about 200,000 real-world interactions, and the team believes it’s the largest real-world, multimodal, off-road driving dataset. The data could later be used to train a self-driving vehicle for off-road navigation.
Wenshan Wang is a project scientist in the Robotics Institute (RI).