vod-devkit 1.0.2

The official devkit of the View-of-Delft Prediction dataset.

The View-of-Delft Prediction devkit

Welcome to the View-of-Delft Prediction (VoD-P) development kit. This repository contains the code and documentation associated with the VoD-P dataset.

Overview

• Introduction
• Changelog
• Devkit setup
• VoD-P
  • VoD-P setup
  • Getting started with VoD-P
  • Submitting to the VoD-P leaderboard
• Known issues
• Citation

Introduction

The View-of-Delft Prediction dataset is an extension of the View-of-Delft dataset. It contains the 3D object annotations of the original dataset and additionally provides accurate 6-DoF global localisation and semantic map data.

The dataset is available in a format based on the nuScenes dataset, and hence this development kit is a modified version of the nuScenes devkit.

Changelog

[202-09-11] Released the View-of-Delft Prediction dataset and development kit.

Devkit setup

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

Our devkit is available and can be installed via pip :

pip install vod-devkit

For an advanced installation, see installation for detailed instructions.

VoD-P

VoD-P setup

To download VoD-P, follow the instructions at the main View-of-Delft dataset page. Download the zipfile when you receive the access link. Unzip the file and you should have the following folder structure:

/data/sets/vod
    maps	-	Folder for all map files (vectorized .json files).
    v1.0-*	-	JSON tables that include all the meta data and annotations. Each split (trainval, test) is provided in a separate folder.

Getting started with VoD-P

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

• Read the main dataset page.
• Request access to the dataset.
• Download the dataset.
• Get the vod-devkit code.
• Read the tutorials or run one yourself using:

jupyter notebook $HOME/vod-devkit/python-sdk/tutorials/vod_tutorial.ipynb

• Read the VoD-P paper for a closer look at the dataset.
• See the FAQs.

Submitting to the VoD-P leaderboard

We will launch a challenge with a public leaderboard soon.

In the meantime, you can evaluate your prediction model locally on the released test set.

Known issues

N/A

Citation

Please use the following citation when referencing the View-of-Delft (VoD-P) dataset:

@article{boekema2024vodp,
  author={Boekema, Hidde J-H. and Martens, Bruno K.W. and Kooij, Julian F.P. and Gavrila, Dariu M.},
  journal={IEEE Robotics and Automation Letters}, 
  title={Multi-class Trajectory Prediction in Urban Traffic using the View-of-Delft Prediction Dataset}, 
  year={2024},
  volume={9},
  number={5},
  pages={4806-4813},
  keywords={Trajectory;Roads;Annotations;Semantics;Pedestrians;Predictive models;History;Datasets for Human Motion;Data Sets for Robot Learning;Deep Learning Methods},
  doi={10.1109/LRA.2024.3385693}}

The View-of-Delft (VoD) dataset can be referenced using:

@ARTICLE{apalffy2022,
  author={Palffy, Andras and Pool, Ewoud and Baratam, Srimannarayana and Kooij, Julian F. P. and Gavrila, Dariu M.},
  journal={IEEE Robotics and Automation Letters}, 
  title={Multi-Class Road User Detection With 3+1D Radar in the View-of-Delft Dataset}, 
  year={2022},
  volume={7},
  number={2},
  pages={4961-4968},
  doi={10.1109/LRA.2022.3147324}}