Naturalistic Trajectories of Vehicles and Vulnerable Road Users Recorded at German Intersections
The inD dataset is a new dataset of naturalistic vehicle trajectories recorded at German intersections. Using a drone, typical limitations of established traffic data collection methods like occlusions are overcome. Traffic was recorded at four different locations. The trajectory for each road user and its type is extracted. Using state-of-the-art computer vision algorithms, the positional error is typically less than 10 centimetres. The dataset is applicable on many tasks such as road user prediction, driver modeling, scenario-based safety validation of automated driving systems or data-driven development of HAD system components.
The dataset includes:
- Vehicles
- Pedestrians
- Bicyclists
The dataset features:
- Four different recording locations
- Different intersection types
- Typical positioning error <10 cm
Provided scripts for Python:
- Parising of provided files
- Visualization of recorded trajectories
Our paper introducing the dataset and the used methods is published on arXiv.org: here. To reference the dataset, please use:
@inproceedings{inDdataset,
title={The inD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories at German Intersections},
author={Bock, Julian and Krajewski, Robert and Moers, Tobias and Vater, Lennart and Runde, Steffen and Eckstein, Lutz},
journal={arXiv preprint arXiv:1911.07602},
year={2019}}