Determining the Workload of Driving Scenarios using Ratings to Support Safety and Usability Assessments

Paul Green

2022

Abstract

How long will it take a driver to take over if the automation fails? Is a particular driver interface too distracting? How comparable are the workloads from 2 studies that involve different roads and traffic? The answer to these driving safety related questions depends upon the workload drivers experience, which should be calculable from data or descriptions of road geometry and traffic. For this purpose, 24 subjects rated the workload of 200 driving scenarios on a 0 to 100 scale. Those scenarios were combinations of road type (urban, rural, expressways, residential streets), traffic, road geometry, the lane driven, and other factors (e.g., 4-lane, straight rural road with 8-foot paved shoulder and 8-foot grass strip beyond that). Finding 1: Those ratings were found to be reliable and well correlated (r=0.75) with ratings collected using the anchored-clip rating method. Finding 2: Workload was predicted by an additive model that used a table of values provided herein. (For example, for urban roads, add 9 points to the base rating for heavy traffic, but 12 points for expressways.) In fact, traffic consistently had the largest effect on workload ratings, with the difference between no traffic and heavy traffic being 50 %.

Paper Citation

in Harvard Style

Green P. (2022). Determining the Workload of Driving Scenarios using Ratings to Support Safety and Usability Assessments. In Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS, ISBN 978-989-758-573-9, pages 96-104. DOI: 10.5220/0011072200003191

in Bibtex Style

@conference{vehits22,
author={Paul Green},
title={Determining the Workload of Driving Scenarios using Ratings to Support Safety and Usability Assessments},
booktitle={Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,},
year={2022},
pages={96-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011072200003191},
isbn={978-989-758-573-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Vehicle Technology and Intelligent Transport Systems - Volume 1: VEHITS,
TI - Determining the Workload of Driving Scenarios using Ratings to Support Safety and Usability Assessments
SN - 978-989-758-573-9
AU - Green P.
PY - 2022
SP - 96
EP - 104
DO - 10.5220/0011072200003191