Cargando…

Long-Term Evaluation of Drivers’ Behavioral Adaptation to an Adaptive Collision Avoidance System

OBJECTIVE: Taking human factors approach in which the human is involved as a part of the system design and evaluation process, this paper aims to improve driving performance and safety impact of driver support systems in the long view of human–automation interaction. BACKGROUND: Adaptive automation...

Descripción completa

Detalles Bibliográficos
Autores principales: Muslim, Husam, Itoh, Makoto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521345/
https://www.ncbi.nlm.nih.gov/pubmed/32484749
http://dx.doi.org/10.1177/0018720820926092
_version_ 1784584880527507456
author Muslim, Husam
Itoh, Makoto
author_facet Muslim, Husam
Itoh, Makoto
author_sort Muslim, Husam
collection PubMed
description OBJECTIVE: Taking human factors approach in which the human is involved as a part of the system design and evaluation process, this paper aims to improve driving performance and safety impact of driver support systems in the long view of human–automation interaction. BACKGROUND: Adaptive automation in which the system implements the level of automation based on the situation, user capacity, and risk has proven effective in dynamic environments with wide variations of human workload over time. However, research has indicated that drivers may not efficiently deal with dynamically changing system configurations. Little effort has been made to support drivers’ understanding of and behavioral adaptation to adaptive automation. METHOD: Using a within-subjects design, 42 participants completed a four-stage driving simulation experiment during which they had to gradually interact with an adaptive collision avoidance system while exposed to hazardous lane-change scenarios over 1 month. RESULTS: Compared to unsupported driving (stage i), although collisions have been significantly reduced when first experienced driving with the system (stage ii), improvements in drivers’ trust in and understanding of the system and driving behavior have been achieved with more driver–system interaction and driver training during stages iii and iv. CONCLUSION: While designing systems that take into account human skills and abilities can go some way to improving their effectiveness, this alone is not sufficient. To maximize safety and system usability, it is also essential to ensure appropriate users’ understanding and acceptance of the system. APPLICATION: These findings have important implications for the development of active safety systems and automated driving.
format Online
Article
Text
id pubmed-8521345
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher SAGE Publications
record_format MEDLINE/PubMed
spelling pubmed-85213452021-10-19 Long-Term Evaluation of Drivers’ Behavioral Adaptation to an Adaptive Collision Avoidance System Muslim, Husam Itoh, Makoto Hum Factors Surface Transportation OBJECTIVE: Taking human factors approach in which the human is involved as a part of the system design and evaluation process, this paper aims to improve driving performance and safety impact of driver support systems in the long view of human–automation interaction. BACKGROUND: Adaptive automation in which the system implements the level of automation based on the situation, user capacity, and risk has proven effective in dynamic environments with wide variations of human workload over time. However, research has indicated that drivers may not efficiently deal with dynamically changing system configurations. Little effort has been made to support drivers’ understanding of and behavioral adaptation to adaptive automation. METHOD: Using a within-subjects design, 42 participants completed a four-stage driving simulation experiment during which they had to gradually interact with an adaptive collision avoidance system while exposed to hazardous lane-change scenarios over 1 month. RESULTS: Compared to unsupported driving (stage i), although collisions have been significantly reduced when first experienced driving with the system (stage ii), improvements in drivers’ trust in and understanding of the system and driving behavior have been achieved with more driver–system interaction and driver training during stages iii and iv. CONCLUSION: While designing systems that take into account human skills and abilities can go some way to improving their effectiveness, this alone is not sufficient. To maximize safety and system usability, it is also essential to ensure appropriate users’ understanding and acceptance of the system. APPLICATION: These findings have important implications for the development of active safety systems and automated driving. SAGE Publications 2020-06-02 2021-11 /pmc/articles/PMC8521345/ /pubmed/32484749 http://dx.doi.org/10.1177/0018720820926092 Text en Copyright © 2020, The Author(s) https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Surface Transportation
Muslim, Husam
Itoh, Makoto
Long-Term Evaluation of Drivers’ Behavioral Adaptation to an Adaptive Collision Avoidance System
title Long-Term Evaluation of Drivers’ Behavioral Adaptation to an Adaptive Collision Avoidance System
title_full Long-Term Evaluation of Drivers’ Behavioral Adaptation to an Adaptive Collision Avoidance System
title_fullStr Long-Term Evaluation of Drivers’ Behavioral Adaptation to an Adaptive Collision Avoidance System
title_full_unstemmed Long-Term Evaluation of Drivers’ Behavioral Adaptation to an Adaptive Collision Avoidance System
title_short Long-Term Evaluation of Drivers’ Behavioral Adaptation to an Adaptive Collision Avoidance System
title_sort long-term evaluation of drivers’ behavioral adaptation to an adaptive collision avoidance system
topic Surface Transportation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8521345/
https://www.ncbi.nlm.nih.gov/pubmed/32484749
http://dx.doi.org/10.1177/0018720820926092
work_keys_str_mv AT muslimhusam longtermevaluationofdriversbehavioraladaptationtoanadaptivecollisionavoidancesystem
AT itohmakoto longtermevaluationofdriversbehavioraladaptationtoanadaptivecollisionavoidancesystem