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nuScenes by Aptiv

Large-scale open source dataset for autonomous driving.

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Overview

Support for computer vision and autonomous driving research

Nutonomy Car 1
Nutonomy Car 2

Data Collection

Careful scene planning by Aptiv

Car Setup

Vehicle, Sensor and Camera Details

    • 12Hz capture frequency
    • 1/1.8'' CMOS sensor of 1600x1200 resolution
    • Bayer8 format for 1 byte per pixel encoding
    • 1600x900 ROI is cropped from the original resolution to reduce processing and transmission bandwidth
    • Auto exposure with exposure time limited to the maximum of 20 ms
    • Images are unpacked to BGR format and compressed to JPEG
    • See camera orientation and overlap in the figure below.
    6
    Cameras
    • 20Hz capture frequency
    • 32 channels
    • 360° Horizontal FOV, +10° to -30° Vertical FOV
    • 80m-100m Range, Usable returns up to 70 meters, ± 2 cm accuracy
    • Up to ~1.39 Million Points per Second
    1
    Spinning LiDAR
    • 77GHz
    • 13Hz capture frequency
    • Independently measures distance and velocity in one cycle using Frequency Modulated Continuous Wave
    • Up to 250m distance
    • Velocity accuracy of ±0.1 km/h
    5
    Long Range RADAR Sensor

Flythrough of the Nuscenes Teaser

Flythrough of the Nuscenes Teaser

Sensor Calibration

Data alignment between sensors and cameras

  • LiDAR extrinsics

  • Camera extrinsics

  • Camera intrinsic calibration

  • IMU extrinsics

Car Sensors

Sensor Synchronization

In order to achieve cross-modality data alignment between the LiDAR and the cameras, the exposure on each camera was triggered when the top LiDAR sweeps across the center of the camera’s FOV. This method was selected as it generally yields good data alignment. Note that the cameras run at 12Hz while the LiDAR runs at 20Hz.

The 12 camera exposures are spread as evenly as possible across the 20 LiDAR scans, so not all LiDAR scans have a correspondingcamera frame.

Reducing the frame rate of the cameras to 12Hz helps to reduce the compute, bandwidth and storage requirement of the perception system.

Sensor Synchronization Capture

Data Annotation

Complex Label Taxonomy

Scene 1

Instances Per Label

493,322

vehicle.car

208,240

human.pedestrian.adult

152,087

movable_object.barrier

208,240

movable_object.trafficcone

88,519

vehicle.truck

24,860

vehicle.trailer

14,671

vehicle.construction

14,501

vehicle.bus.rigid

More details →

Get Started with Nuscenes

Ready to get started with nuScenes?

This tutorial will give you an overview of the dataset without the need to download it.

Please note that this page is a rendered version of a Jupyter Notebook.

Take the Tutorial