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: 10.4121/16847203

Olger Siebinga; Zgonnikov, Arkady; Abbink, D.A. (David) (2021): Code accompanying the paper "Validating human driver models for interaction-aware automated vehicle controllers: A human approach”. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/16847203.v1

Software

• Mechanical Engineering
• Other Engineering
• Automotive Engineering

automated driving, driver model validation, interaction-aware controllers, inverse-reinforcement-learning driver-model

GPL-3.0

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

by Olger Siebinga , Arkady Zgonnikov, D.A. (David) Abbink

This python package contains scripts needed to train IRL Driver models on HighD datasets. This code is accompanying the paper "Validating human driver models for interaction-aware automated vehicle controllers: A human factors approach - Siebinga, Zgonnikov & Abbink 2021" and should be used in combination with TraViA, a program for traffic data visualization and annotation. A preprint of this paper can be found on arxiv: https://arxiv.org/abs/2109.13077

• 2021-10-22 first online, published, posted

4TU.ResearchData

.py; .md

• https://github.com/tud-hri/irlmodelvalidation
• https://arxiv.org/abs/2109.13077

TU Delft, Faculty of Mechanical, Maritime and Materials Engineering