nuScenes devkit

Welcome to the devkit of the nuScenes dataset.

Overview

Changelog
Dataset download
Map expansion
Devkit setup
Getting started
Citation

Changelog

Mar. 24, 2020: Devkit v1.0.7: nuScenes prediction challenge code released.
Feb. 12, 2020: Devkit v1.0.6: CAN bus expansion released.
Dec. 11, 2019: Devkit v1.0.5: Remove weight factor from AMOTA tracking metrics.
Nov. 1, 2019: Tracking eval code released and detection eval code reorganized.
Jul. 1, 2019: Map expansion released.
Apr. 30, 2019: Devkit v1.0.1: loosen PIP requirements, refine detection challenge, export 2d annotation script.
Mar. 26, 2019: Full dataset, paper, & devkit v1.0.0 released. Support dropped for teaser data.
Dec. 20, 2018: Initial evaluation code released. Devkit folders restructured, which breaks backward compatibility.
Nov. 21, 2018: RADAR filtering and multi sweep aggregation.
Oct. 4, 2018: Code to parse RADAR data released.
Sep. 12, 2018: Devkit for teaser dataset released.

Dataset download

To download nuScenes you need to go to the Download page, create an account and agree to the nuScenes Terms of Use. After logging in you will see multiple archives. For the devkit to work you will need to download all archives. Please unpack the archives to the /data/sets/nuscenes folder *without* overwriting folders that occur in multiple archives. Eventually you should have the following folder structure:

/data/sets/nuscenes
    samples	-	Sensor data for keyframes.
    sweeps	-	Sensor data for intermediate frames.
    maps	-	Folder for all map files: rasterized .png images and vectorized .json files.
    v1.0-*	-	JSON tables that include all the meta data and annotations. Each split (trainval, test, mini) is provided in a separate folder.

If you want to use another folder, specify the dataroot parameter of the NuScenes class (see tutorial).

Prediction Challenge

In March 2020 we released code for the nuScenes prediction challenge. To get started:

Download the version 1.2 of the map expansion (see below).
Download the trajectory sets for CoverNet from here.
Go through the tutorial.
For information on how submissions will be scored, visit the challenge website.

CAN bus expansion

In February 2020 we published the CAN bus expansion. It contains low-level vehicle data about the vehicle route, IMU, pose, steering angle feedback, battery, brakes, gear position, signals, wheel speeds, throttle, torque, solar sensors, odometry and more. To install this expansion, please follow these steps:

Download the expansion from the Download page,
Move the can_bus folder to your nuScenes root directory (e.g. /data/sets/nuscenes/can_bus).
Get the latest version of the nuscenes-devkit.
If you already have a previous version of the devkit, update the pip requirements (see details): pip install -r setup/requirements.txt
Get started with the readme or tutorial.

Map expansion

In July 2019 we published a map expansion with 11 semantic layers (crosswalk, sidewalk, traffic lights, stop lines, lanes, etc.). To install this expansion, please follow these steps:

Download the expansion from the Download page,
Move the .json files to your nuScenes maps folder.
Get the latest version of the nuscenes-devkit.
If you already have a previous version of the devkit, update the pip requirements (see details): pip install -r setup/requirements.txt
Get started with the tutorial.

Devkit setup

The devkit is tested for Python 3.6 and Python 3.7. To install Python, please check here.

Our devkit is available and can be installed via pip :

pip install nuscenes-devkit

For an advanced installation, see installation for detailed instructions.

Getting started

Please follow these steps to make yourself familiar with the nuScenes dataset:

Read the dataset description.
Explore the lidar viewer and videos.
Download the dataset.
Get the nuscenes-devkit code.
Read the online tutorial or run it yourself using:

jupyter notebook $HOME/nuscenes-devkit/python-sdk/tutorials/nuscenes_basics.ipynb

Read the nuScenes paper for a detailed analysis of the dataset.
Run the map expansion tutorial.
Take a look at the experimental scripts.
For instructions related to the object detection task (results format, classes and evaluation metrics), please refer to this readme.
See the database schema and annotator instructions.
See the FAQs.

Citation

Please use the following citation when referencing nuScenes:

@article{nuscenes2019,
  title={nuScenes: A multimodal dataset for autonomous driving},
  author={Holger Caesar and Varun Bankiti and Alex H. Lang and Sourabh Vora and 
          Venice Erin Liong and Qiang Xu and Anush Krishnan and Yu Pan and 
          Giancarlo Baldan and Oscar Beijbom},
  journal={arXiv preprint arXiv:1903.11027},
  year={2019}
}

EloiZ/nuscenes-devkit