Waymo Releases a Self-Driving Open Data Set For Free Use By Research Community

An anonymous reader quotes a report from TechCrunch: Waymo is opening up its significant stores of autonomous driving data with a new Open Data Set it's making available for the purposes of research. The data set isn't for commercial use, but its definition of "research" is fairly broad, and includes researchers at other companies as well as academics. The data set is "one of the largest, riches and most diverse self-driving data sets ever released for research," according to Waymo principal scientist and head of Research, Drago Anguelov, who was at both Zoox and Google prior to joining Waymo last year. Anguelov said in a briefing that the reason he initiated the push to make this data available is that Waymo and several other companies working in the field are "currently hampered by the lack of suitable data sets."

The Waymo Open Data set tries to fill in some of these gaps for their research peers by providing data collected from 1,000 driving segments done by its autonomous vehicles on roads, with each segment representing 20 seconds of continuous driving. It includes driving done in Phoenix, Ariz.; Kirkland, Wash.; Mountain View, Calif.; and San Francisco, Calif., and offering a range of different driving conditions, including at night, during rain, at dusk and more. The segments include data collected from five of Waymo's own proprietary lidars, as well as five standard cameras that face front and to the sides, providing a 360-degree view captured in high resolution, as well as synchronization Waymo uses to fuse lidar and imaging data. Objects, including vehicles, pedestrians, cyclists and signage is all labeled. "We decided to contribute our part to make, ultimately, researchers in academia ask the right questions -- and for that, they need the right data," Anguelov said. "And I think this will help everyone in the field; it is not an admission in any way that we have problems solving these issues. But there is always room for improvement in terms of efficiency, scaleability, amount of labels to need. It's a developing field. Mostly we're trying to get others into thinking about our problems and working with us, as opposed to doing work that's potentially not so impactful, given the current state of things."