vod-devkit 1.1.2

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
• 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

• [2024-11-20] Launched the View-of-Delft Prediction leaderboard.
• [2024-11-15] Released a version of the development kit for Python 3.8.
• [2024-09-11] Released the View-of-Delft Prediction dataset and development kit.

Devkit setup

The devkit is tested for Python 3.8. For a version of the devkit that is compatible with Python 3.6 and 3.7, see the v1.0.* PyPI releases or tags. 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/tutorials/vod_tutorial.ipynb

• Read the View-of-Delft Prediction paper for a closer look at the dataset.
• See the FAQs.

Submitting to the VoD-P leaderboard

The VoD-P benchmark leaderboard can be found at https://eval.ai/web/challenges/challenge-page/2410/overview.

See the benchmark instructions for the submission format and rules.

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}}