PandaSet

Public large-scale dataset for autonomous driving provided by Hesai & Scale. It enables researchers to study challenging urban driving situations using the full sensor suit of a real self-driving-car.

  • Scene #1
  • Scene #2
  • Scene #3
  • Scene #4
  • Scene #5
  • Scene #6
  • Scene #7
  • Scene #8
Overview

Sophisticated LiDAR technology meets high-quality data annotation

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.

Car Setup

Vehicle, Sensor and Camera Details

We collected data using a Chrysler Pacifica minivan mounted with a suite of cameras and Hesai LiDARs.

    • 10 Hz capture frequency
    • 1/2.7” CMOS sensor of 1920x1080 resolution
    • Images are unpacked to YUV 4:4:4 format and compressed to JPEG
    5
    Wide-Angle Cameras
    • 10 Hz capture frequency
    • 1/2.7” CMOS sensor of 1920x1080 resolution
    • Images are unpacked to YUV 4:4:4 format and compressed to JPEG
    1
    Long-Focus Camera
    • 1x Spinning LiDARs
    • 64 channels
    • 200m range @ 10% reflectivity
    • 360° horizontal FOV; 40° vertical FOV (-25° to +15°)
    • 0.2° horizontal angular resolution (10 Hz); 0.167° vertical angular resolution (finest)
    • 10 Hz capture frequency
    1
    Pandar64: Mechanical Spinning LiDAR
    • Equivalent to 150 channels at 10 Hz
    • 300m range @ 10% reflectivity
    • 60° horizontal FOV; 20° vertical FOV (-10° to +10° with ±5° offset, configurable)
    • 0.1° horizontal angular resolution; 0.07° vertical angular resolution (finest) at 10 Hz
    • 10 Hz capture frequency
    1
    PandarGT: Solid-State LiDAR

PandarGT Road Test


Sensor Calibration

Data alignment between sensors and cameras

To achieve a high quality multi-sensor dataset, it is essential to calibrate the extrinsics and intrinsics of every sensor.

We express extrinsic coordinates relative to the ego frame, i.e. the midpoint of the rear vehicle axle.

The most relevant steps are described below:

  • LiDAR extrinsics

  • Camera extrinsics

  • Camera intrinsic calibration

  • IMU extrinsics

Car Sensors
Data Annotation

Complex Label Taxonomy

Scale’s data annotation platform combines human work and review with smart tools, statistical confidence checks and machine learning checks to ensure the quality of annotations.

The resulting accuracy is consistently higher than what a human or synthetic labeling approach can achieve independently as measured against seven rigorous quality areas for each annotation.

PandaSet includes 3D Bounding boxes for 28 object classes and a rich set of class attributes related to activity, visibility, location, pose. The dataset also includes Point Cloud Segmentation with 37 semantic labels including for smoke, car exhaust, vegetation, and driveable surface.

Scene #1

Get Started with PandaSet

High-quality open-source dataset for autonomous driving.