What happens when an autonomous vehicle gets stuck in the mud?
Researchers from Carnegie Mellon University wanted to find out. They took an all-terrain vehicle off-road. With researchers on board, it traveled through challenging situations such as driving through dense vegetation and puddles. It was put through the paces with aggressive driving up and down hills, at speeds of 30mph (48mph), and sharp turns.
The researchers generated a dataset called TartanDrive. It consists of roughly 200,000 off-road driving interactions on a modified Yamaha Viking ATV. This included seven unique sensing modalities in diverse terrains. They believe this to be the largest real-world, multimodal, off-road driving dataset in terms of interactions and sensor types.
In the future, automakers could use the data to train autonomous off-road vehicles.
Most research focuses on urban environments. In a literature review, the researchers note that current off-ride driving sets tend to focus on understanding environmental features instead of the interplay between the robot and the environment.
So this data provides a valuable resource for future research.
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