Welcome to the nuTonomy® downloadable driverless vehicle software page. Click on the green box above labeled "Code" to download a copy of the software described below.
We use a common devkit for nuScenes and nuImages. 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.
nuImages is a stand-alone large-scale image dataset. It uses the same sensor setup as the 3d nuScenes dataset. The structure is similar to nuScenes and both use the same devkit, which make the installation process simple.
To download nuImages you need to go to the Download page,
create an account and agree to the nuScenes Terms of Use.
For the devkit to work you will need to download at least the metadata and samples, the sweeps are optional.
Please unpack the archives to the /data/sets/nuimages
folder *without* overwriting folders that occur in multiple archives.
Eventually you should have the following folder structure:
/data/sets/nuimages
samples - Sensor data for keyframes (annotated images).
sweeps - Sensor data for intermediate frames (unannotated images).
v1.0-* - JSON tables that include all the meta data and annotations. Each split (train, val, test, mini) is provided in a separate folder.
If you want to use another folder, specify the dataroot
parameter of the NuImages class (see tutorial).
Please follow these steps to make yourself familiar with the nuImages dataset:
jupyter notebook $HOME/nuscenes-devkit/python-sdk/tutorials/nuimages_tutorial.ipynb
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).
In August 2021 we published Panoptic nuScenes which contains the panoptic labels of the point clouds for the approximately 40,000 keyframes in nuScenes. To install Panoptic nuScenes, please follow these steps:
panoptic
and v1.0-*
folders to your nuScenes root directory (e.g. /data/sets/nuscenes/panoptic
, /data/sets/nuscenes/v1.0-*
).In August 2020 we published nuScenes-lidarseg which contains the semantic labels of the point clouds for the approximately 40,000 keyframes in nuScenes. To install nuScenes-lidarseg, please follow these steps:
lidarseg
and v1.0-*
folders to your nuScenes root directory (e.g. /data/sets/nuscenes/lidarseg
, /data/sets/nuscenes/v1.0-*
).pip install -r setup/requirements.txt
In March 2020 we released code for the nuScenes prediction challenge. To get started:
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:
/data/sets/nuscenes/can_bus
).pip install -r setup/requirements.txt
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:
basemap
, expansion
and prediction
) to your nuScenes maps
folder.pip install -r setup/requirements.txt
Here we give a brief overview of the different map versions:
Please follow these steps to make yourself familiar with the nuScenes dataset:
jupyter notebook $HOME/nuscenes-devkit/python-sdk/tutorials/nuscenes_tutorial.ipynb
Great care has been taken to collate the nuScenes dataset and many users have praised the quality of the data and annotations. However, some minor issues remain:
Maps:
Annotations:
Please use the following citation when referencing nuScenes or nuImages:
@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}
}
Please use the following citation when referencing Panoptic nuScenes or nuScenes-lidarseg:
@article{fong2021panoptic,
title={Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Tracking},
author={Fong, Whye Kit and Mohan, Rohit and Hurtado, Juana Valeria and Zhou, Lubing and Caesar, Holger and
Beijbom, Oscar and Valada, Abhinav},
journal={arXiv preprint arXiv:2109.03805},
year={2021}
}