truckscenes-devkit 1.0.0

Official development kit of the MAN TruckScenes dataset (www.man.eu/truckscenes).

MAN TruckScenes devkit

The first multimodal dataset for autonomous trucking

Overview

• Installation
• Setup
• Usage
• Citation

Installation

Our devkit is available and can be installed via pip:

pip install truckscenes-devkit

If you also want to install all the (optional) dependencies for running the visualizations:

pip install "truckscenes-devkit[all]"

The usage requires Python (install here, tested for 3.6 and 3.8) and pip (install here) for the installation.

TruckScenes Setup

The MAN TruckScenes dataset can be downloaded on our Download page or search for TruckScenes in the AWS Open Data Registry.

For the devkit to work you will need to download all archives.
Please unpack the archives to the /data/truckscenes folder without overwriting folders that occur in multiple archives.
Eventually you should have the following folder structure:

/data/truckscenes
    samples	-	Sensor data for keyframes.
    sweeps	-	Sensor data for intermediate frames.
    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 TruckScenes class (see installation).

TruckScenes Usage

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

• Download the dataset on our website.
• Make yourself familiar with the dataset schema
• Run the tutorial to get started:

  jupyter notebook $HOME/truckscenes-devkit/tutorials/truckscenes_tutorial.ipynb
  

• Read the MAN TruckScenes Paper for a detailed analysis of the dataset.

Citation

@article{truckscenes2024,
  title={MAN TruckScenes: A multimodal dataset for autonomous trucking in diverse conditions},
  author={Fent, Felix and Kuttenreich, Fabian and Ruch, Florian and Rizwin, Farija and
    Juergens, Stefan and Lechermann, Lorenz and Nissler, Christian and Perl, Andrea and
    Voll, Ulrich and Yan, Min and Lienkamp, Markus},
  journal={arXiv preprint arXiv:2407.07462},
  year={2024}
}

Copied and adapted from nuscenes-devkit