AMI: Attention based Adaptative Feedback with Augmented Reality to Improve Takeover Performances in Highly Automated Vehicles

Baptiste Wojtkowski, Indira Thouvenin, Veronica Teichrieb

2022

Abstract

In the coming decade, the level 3 of semi-autonomous vehicles on the SAE scale is set to develop. However, the question of the transition of control between human and vehicle remains a widely debated question. From a cognitive point of view, this operation consists of placing the user back in a sensorimotor loop while limiting cognitive overload. In order to reduce this, several augmented reality / mixed reality approaches have been carried out. In this preliminary study, we propose an approach based on adaptive feedback. A naive adaptation model based on the work of Herzberger is introduced, studying the behavior of the user through his head behavior to determine an attention level. We carried out an experiment in a driving simulator reproducing a highway in virtual reality and displaying AR feedback through the virtual environment. The experiment tends to show that users perform better when they are placed in front of adaptive feedback. In a future work, we plan to complicate this model.

Paper Citation

in Harvard Style

Wojtkowski B., Thouvenin I. and Teichrieb V. (2022). AMI: Attention based Adaptative Feedback with Augmented Reality to Improve Takeover Performances in Highly Automated Vehicles. In Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 2: HUCAPP; ISBN 978-989-758-555-5, SciTePress, pages 99-107. DOI: 10.5220/0010914400003124

in Bibtex Style

@conference{hucapp22,
author={Baptiste Wojtkowski and Indira Thouvenin and Veronica Teichrieb},
title={AMI: Attention based Adaptative Feedback with Augmented Reality to Improve Takeover Performances in Highly Automated Vehicles},
booktitle={Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 2: HUCAPP},
year={2022},
pages={99-107},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010914400003124},
isbn={978-989-758-555-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2022) - Volume 2: HUCAPP
TI - AMI: Attention based Adaptative Feedback with Augmented Reality to Improve Takeover Performances in Highly Automated Vehicles
SN - 978-989-758-555-5
AU - Wojtkowski B.
AU - Thouvenin I.
AU - Teichrieb V.
PY - 2022
SP - 99
EP - 107
DO - 10.5220/0010914400003124
PB - SciTePress