Welcome to the homepage of the View-of-Delft (VoD) dataset, a novel automotive dataset recorded in Delft, the Netherlands. It contains 8600+ frames of synchronized and calibrated 64-layer LiDAR-, (stereo) camera-, and 3+1D  (range, azimuth, elevation, + Doppler) radar-data acquired in complex, urban traffic. It consists of 123100+ 3D bounding box annotations of both moving and static objects, including 26500+ pedestrian, 10800 cyclist and 26900+ car labels. It additionally contains semantic map annotations and accurate ego-vehicle localization data.

Benchmarks for detection and prediction tasks are released for the dataset. See the sections below for details on these benchmarks.

Access

The dataset is made freely available for non-commercial research purposes only. Eligibility to use the dataset is limited to Master- and PhD-students, and staff of academic and non-profit research institutions. Access can be requested through this form:

Form to request access to the VoD dataset

By requesting access, the researcher agrees to use and handle the data according to the license. See furthermore our privacy statement.

After validating the researcher’s association with a research institute, we will send an email containing password-protected download link(s) for the VoD dataset. Sharing these links and/or the passwords is strictly forbidden (see the license).

In case of questions or problems, please send an email to viewofdelftdataset@gmail.com.

Frequently asked questions about the license:

• Q: Is it possible for MSc and PhD students to use the dataset if they work/cooperate with a for-profit organization?
  A: The current VoD license permits the use of the VoD dataset of a MS/PhD student on the compute facilities (storing, processing) of his/her academic institution for research towards his/her degree – even if this MS/PhD student is (also) employed by a company.
  The license does not permit the use of the VoD dataset on the compute facilities (storing, processing) of a for-profit organization.

View-of-Delft Detection Benchmark

An object detection benchmark is available for researchers to develop and evaluate their models on the VoD dataset. At the time of publication, this benchmark was the largest automotive multi-class object detection dataset containing 3+1D radar data, and the only dataset containing high-end (64-layer) LiDAR and (any kind of) radar data at the same time.

To view the leaderboard or make a submission to the benchmark, please visit:

https://eval.ai/web/challenges/challenge-page/2380/overview

For the documentation, development kit, and example code for the detection benchmark, please visit:

https://tudelft-iv.github.io/view-of-delft-dataset

If you use the detection benchmark in your research, please cite us as:

@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}}

View-of-Delft Prediction Benchmark

A trajectory prediction benchmark is also publicly available to enable research on urban multi-class trajectory prediction. This benchmark contains challenging prediction cases in the historic city center of Delft with a high proportion of Vulnerable Road Users (VRUs), such as pedestrians and cyclists. Semantic map annotations for road elements such as lanes, sidewalks, and crosswalks are provided as context for prediction models.

To view the leaderboard or make a submission to the benchmark, please visit:

https://eval.ai/web/challenges/challenge-page/2410/overview

For the documentation, development kit, and example code for the prediction benchmark, please visit:

https://github.com/tudelft-iv/view-of-delft-prediction-devkit

If you use the prediction benchmark in your research, please cite us as:

@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}}