swMATH ID: 36636
Software Authors: John Houston, Guido Zuidhof, Luca Bergamini, Yawei Ye, Long Chen, Ashesh Jain, Sammy Omari, Vladimir Iglovikov, Peter Ondruska
Description: L5Kit dataset: One Thousand and One Hours: Self-driving Motion Prediction Dataset. 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 https://self-driving.lyft.com/level5/
Homepage: https://self-driving.lyft.com/level5/
Source Code:  https://github.com/lyft/l5kit
Keywords: Dataset; Self-driving; Motion prediction
Related Software: BDD100k; H3D; ApolloScape; Mapillary Vistas; KITTI; nuScenes; WoodScape; CityPersons; NightOwls; TrafficPredict; PointPillars; A2D2; PointRNN; Talk2Car; ImageNet; Argoverse; MonoLoco; BoundingBoxes; Drive&Act; LabelMe
Cited in: 0 Publications