PandaSet aims to promote and advance research and development in autonomous driving and machine learning.

The first open-source dataset made available for both academic and commercial use, PandaSet combines Hesai¡¯s best-in-class LiDAR sensors with Scale AI¡¯s high-quality data annotation. PandaSet features data collected using a forward-facing LiDAR with image-like resolution (PandarGT) as well as a mechanical spinning LiDAR (Pandar64). The collected data was annotated with a combination of cuboid and segmentation annotation (Scale 3D Sensor Fusion Segmentation).

It features:

• 48,000 camera images
• 16,000 LiDAR sweeps
• +100 scenes of 8s each
• 28 annotation classes
• 37 semantic segmentation labels
• Full sensor suite: 1x mechanical LiDAR, 1x solid-state LiDAR, 6x cameras, On-board GPS/IMU

Data Collection

Complex Driving Scenarios in Urban Environments

For PandaSet we carefully planned routes and selected scenes that would showcase complex urban driving scenarios, including steep hills, construction, dense traffic and pedestrians, and a variety of times of day and lighting conditions in the morning, afternoon, dusk and evening.

PandaSet scenes are selected from 2 routes in Silicon Valley: (1) San Francisco; and (2) El Camino Real from Palo Alto to San Mateo.