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Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing
The aim of this study was to quantify the effect of radius over horizontal curve sections on driving workload (DW). Twenty-five participants participated in the driving simulation experiments and completed five driving scenes. The NASA-TLX scale was used to measure the mental demand, physical demand...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222676/ https://www.ncbi.nlm.nih.gov/pubmed/35742312 http://dx.doi.org/10.3390/ijerph19127063 |
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author | Liu, Huan Xu, Jinliang Zhang, Xiaodong Gao, Chao Sun, Rishuang |
author_facet | Liu, Huan Xu, Jinliang Zhang, Xiaodong Gao, Chao Sun, Rishuang |
author_sort | Liu, Huan |
collection | PubMed |
description | The aim of this study was to quantify the effect of radius over horizontal curve sections on driving workload (DW). Twenty-five participants participated in the driving simulation experiments and completed five driving scenes. The NASA-TLX scale was used to measure the mental demand, physical demand, and temporal demand in various scenes, which were applied to assess subjective workload (SW). Objective workload (OW) assessment methods were divided into three types, in which the eye tracker was used to measure the blink frequency and pupil diameter, and the electrocardiograph (ECG) was used to measure the heart rate and the heart rate variability. Additionally, the simulator was used to measure the lateral position and the steering wheel angle. The results indicate that radius is negatively correlated with DW and SW, and the SW in a radius of 300 m is approximately twice that in a radius of 550 m. Compared with the ECG, the explanatory power of the OW can be increased to 0.974 by combining eye-movement, ECG, and driving performance. Moreover, the main source of the DW is the maneuver stage, which accounts for more than 50%. When the radius is over 550 m, the DW shows few differences in the maneuver stage. These findings may provide new avenues of research to harness the role of DWs in optimizing traffic safety. |
format | Online Article Text |
id | pubmed-9222676 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92226762022-06-24 Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing Liu, Huan Xu, Jinliang Zhang, Xiaodong Gao, Chao Sun, Rishuang Int J Environ Res Public Health Article The aim of this study was to quantify the effect of radius over horizontal curve sections on driving workload (DW). Twenty-five participants participated in the driving simulation experiments and completed five driving scenes. The NASA-TLX scale was used to measure the mental demand, physical demand, and temporal demand in various scenes, which were applied to assess subjective workload (SW). Objective workload (OW) assessment methods were divided into three types, in which the eye tracker was used to measure the blink frequency and pupil diameter, and the electrocardiograph (ECG) was used to measure the heart rate and the heart rate variability. Additionally, the simulator was used to measure the lateral position and the steering wheel angle. The results indicate that radius is negatively correlated with DW and SW, and the SW in a radius of 300 m is approximately twice that in a radius of 550 m. Compared with the ECG, the explanatory power of the OW can be increased to 0.974 by combining eye-movement, ECG, and driving performance. Moreover, the main source of the DW is the maneuver stage, which accounts for more than 50%. When the radius is over 550 m, the DW shows few differences in the maneuver stage. These findings may provide new avenues of research to harness the role of DWs in optimizing traffic safety. MDPI 2022-06-09 /pmc/articles/PMC9222676/ /pubmed/35742312 http://dx.doi.org/10.3390/ijerph19127063 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Liu, Huan Xu, Jinliang Zhang, Xiaodong Gao, Chao Sun, Rishuang Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing |
title | Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing |
title_full | Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing |
title_fullStr | Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing |
title_full_unstemmed | Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing |
title_short | Evaluation Method of the Driving Workload in the Horizontal Curve Section Based on the Human Model of Information Processing |
title_sort | evaluation method of the driving workload in the horizontal curve section based on the human model of information processing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9222676/ https://www.ncbi.nlm.nih.gov/pubmed/35742312 http://dx.doi.org/10.3390/ijerph19127063 |
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