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Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems

V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities...

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Detalles Bibliográficos
Autores principales: Ma, Jun, Li, Jiateng, Huang, Hongwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838494/
https://www.ncbi.nlm.nih.gov/pubmed/35161934
http://dx.doi.org/10.3390/s22031189
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author Ma, Jun
Li, Jiateng
Huang, Hongwei
author_facet Ma, Jun
Li, Jiateng
Huang, Hongwei
author_sort Ma, Jun
collection PubMed
description V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles.
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spelling pubmed-88384942022-02-13 Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems Ma, Jun Li, Jiateng Huang, Hongwei Sensors (Basel) Article V2X is used for communication between the surrounding pedestrians, vehicles, and roadside units. In the Forward Collision Warning (FCW) of Phase One scenarios in V2X, multimodal modalities and multiple warning stages are the two main warning strategies of FCW. In this study, three warning modalities were introduced, namely auditory warning, visual warning, and haptic warning. Moreover, a multimodal warning and a novel multi-staged HUD warning were established. Then, the above warning strategies were evaluated in objective utility, driving performance, visual workload, and subjective evaluation. As for the driving simulator of the experiment, SCANeR was adopted to develop the driving scenario and an open-cab simulator was built based on Fanatec hardware. Kinematic parameters, location-related data and eye-tracking data were then collected. The results of the Analysis of Variance (ANOVA) indicate that the multimodal warning is significantly better than that of every single modality in utility and longitudinal car-following performance, and there is no significant difference in visual workload between multimodal warning and the baseline. The utility and longitudinal driving performance of multi-staged warning are also better than those of single-stage warning. Finally, the results provide a reference for the warning strategy design of the FCW in Intelligent Connected Vehicles. MDPI 2022-02-04 /pmc/articles/PMC8838494/ /pubmed/35161934 http://dx.doi.org/10.3390/s22031189 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
Ma, Jun
Li, Jiateng
Huang, Hongwei
Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems
title Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems
title_full Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems
title_fullStr Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems
title_full_unstemmed Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems
title_short Evaluation of Multimodal and Multi-Staged Alerting Strategies for Forward Collision Warning Systems
title_sort evaluation of multimodal and multi-staged alerting strategies for forward collision warning systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838494/
https://www.ncbi.nlm.nih.gov/pubmed/35161934
http://dx.doi.org/10.3390/s22031189
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