  Motivated by the impact of large-scale datasets on ML systems we present the
largest self-driving dataset for motion prediction to date, containing over
1,000 hours of data. This was collected by a fleet of 20 autonomous vehicles
along a fixed route in Palo Alto, California, over a four-month period. It
consists of 170,000 scenes, where each scene is 25 seconds long and captures
the perception output of the self-driving system, which encodes the precise
positions and motions of nearby vehicles, cyclists, and pedestrians over time.
On top of this, the dataset contains a high-definition semantic map with 15,242
labelled elements and a high-definition aerial view over the area. We show that
using a dataset of this size dramatically improves performance for key
self-driving problems. Combined with the provided software kit, this collection
forms the largest and most detailed dataset to date for the development of
self-driving machine learning tasks, such as motion forecasting, motion
planning and simulation. The full dataset is available at
http://level5.lyft.com/.
