Accurate modelling of road user interaction has received lot of attention in recent years due to the advent of increasingly automated vehicles. To support such modelling, there is a need to complement naturalistic datasets of road user interaction with targeted, controlled study data. This paper describes a dataset collected in a simulator study conducted in the project COMMOTIONS, addressing urban driving interactions, in a state of the art moving base driving simulator. The study focused on two types of near-crash situations that can arise in urban driving interactions, and also collected data on human driver gap acceptance across a range of controlled gap sequences.
@article{arxiv.2305.11909,
title = {The COMMOTIONS Urban Interactions Driving Simulator Study Dataset},
author = {Aravinda Ramakrishnan Srinivasan and Julian Schumann and Yueyang Wang and Yi-Shin Lin and Michael Daly and Albert Solernou and Arkady Zgonnikov and Matteo Leonetti and Jac Billington and Gustav Markkula},
journal= {arXiv preprint arXiv:2305.11909},
year = {2024}
}
Comments
5 pages, 8 figures, 6 tables, data techincal description paper, Open Science Foundation - https://osf.io/eazg5/