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