The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/21334548

Julian Schumann (2022): Data and Results for the Benchmark of Gap Acceptance Models. Version 3. 4TU.ResearchData. dataset. https://doi.org/10.4121/21334548.v3

Dataset

x choose version: version 2 - 2022-10-26 version 1 - 2022-10-17

• Ground Transport

Autonomous Vehicles, Benchmark, Gap acceptance, Human behavior prediction, Prediction model, robust AI

CC0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Julian Schumann

This dataset allows the addition of the missing parts of the framework for benchmarking gap accetpance models (https://github.com/julianschumann/Framework-for-benchmarking-gap-acceptance). This are:

- The raw data of the CoR dataset

- The results at every stage of the framework for the implemented modules. The results shown in the corresponding paper are based thereon.

The information on how to include this data in the corresponding code base can be found on the github project README

• 2022-10-17 first online

• 2022-12-05 published, posted

4TU.ResearchData

Two .zip files, including *.csv and *.npy files

• https://github.com/julianschumann/Framework-for-benchmarking-gap-acceptance

TU Delft, Faculty of Mechanical, Maritime and Materials Engineering (3mE), Department of Cognitive Robotics