Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation

This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor...

Full description

Bibliographic Details
Main Authors: Thierry Bellet, Aurélie Banet, Marie Petiot, Bertrand Richard, Joshua Quick
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Information
Subjects:
Online Access:https://www.mdpi.com/2078-2489/12/1/13
id doaj-4a7318bbd3fc49bea847e0643121ba53
record_format Article
spelling doaj-4a7318bbd3fc49bea847e0643121ba532021-01-01T00:02:56ZengMDPI AGInformation2078-24892021-12-0112131310.3390/info12010013Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle AutomationThierry Bellet0Aurélie Banet1Marie Petiot2Bertrand Richard3Joshua Quick4Laboratory of Ergonomics and Cognitive Sciences Applied to Transport, Gustave Eiffel University, LESCOT, 69675 Lyon, FranceLaboratory of Accident Mechanism Analysis, Gustave Eiffel University, LMA, 13300 Salon-de-Provence, FranceLaboratory of Ergonomics and Cognitive Sciences Applied to Transport, Gustave Eiffel University, LESCOT, 69675 Lyon, FranceLaboratory of Ergonomics and Cognitive Sciences Applied to Transport, Gustave Eiffel University, LESCOT, 69675 Lyon, FranceLaboratory of Ergonomics and Cognitive Sciences Applied to Transport, Gustave Eiffel University, LESCOT, 69675 Lyon, FranceThis article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”.https://www.mdpi.com/2078-2489/12/1/13Human-Centered Design (HCD)Human-Centered Artificial Intelligence (HCAI)Vehicle AutomationHuman-Machine Transition (HMT)driver monitoringadaptive HMI
collection DOAJ
language English
format Article
sources DOAJ
author Thierry Bellet
Aurélie Banet
Marie Petiot
Bertrand Richard
Joshua Quick
spellingShingle Thierry Bellet
Aurélie Banet
Marie Petiot
Bertrand Richard
Joshua Quick
Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation
Information
Human-Centered Design (HCD)
Human-Centered Artificial Intelligence (HCAI)
Vehicle Automation
Human-Machine Transition (HMT)
driver monitoring
adaptive HMI
author_facet Thierry Bellet
Aurélie Banet
Marie Petiot
Bertrand Richard
Joshua Quick
author_sort Thierry Bellet
title Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation
title_short Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation
title_full Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation
title_fullStr Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation
title_full_unstemmed Human-Centered AI to Support an Adaptive Management of Human-Machine Transitions with Vehicle Automation
title_sort human-centered ai to support an adaptive management of human-machine transitions with vehicle automation
publisher MDPI AG
series Information
issn 2078-2489
publishDate 2021-12-01
description This article is about the Human-Centered Design (HCD), development and evaluation of an Artificial Intelligence (AI) algorithm aiming to support an adaptive management of Human-Machine Transition (HMT) between car drivers and vehicle automation. The general principle of this algorithm is to monitor (1) the drivers’ behaviors and (2) the situational criticality to manage in real time the Human-Machine Interactions (HMI). This Human-Centered AI (HCAI) approach was designed from real drivers’ needs, difficulties and errors observed at the wheel of an instrumented car. Then, the HCAI algorithm was integrated into demonstrators of Advanced Driving Aid Systems (ADAS) implemented on a driving simulator (dedicated to highway driving or to urban intersection crossing). Finally, user tests were carried out to support their evaluation from the end-users point of view. Thirty participants were invited to practically experience these ADAS supported by the HCAI algorithm. To increase the scope of this evaluation, driving simulator experiments were implemented among three groups of 10 participants, corresponding to three highly contrasted profiles of end-users, having respectively a positive, neutral or reluctant attitude towards vehicle automation. After having introduced the research context and presented the HCAI algorithm designed to contextually manage HMT with vehicle automation, the main results collected among these three profiles of future potential end users are presented. In brief, main findings confirm the efficiency and the effectiveness of the HCAI algorithm, its benefits regarding drivers’ satisfaction, and the high levels of acceptance, perceived utility, usability and attractiveness of this new type of “adaptive vehicle automation”.
topic Human-Centered Design (HCD)
Human-Centered Artificial Intelligence (HCAI)
Vehicle Automation
Human-Machine Transition (HMT)
driver monitoring
adaptive HMI
url https://www.mdpi.com/2078-2489/12/1/13
work_keys_str_mv AT thierrybellet humancenteredaitosupportanadaptivemanagementofhumanmachinetransitionswithvehicleautomation
AT aureliebanet humancenteredaitosupportanadaptivemanagementofhumanmachinetransitionswithvehicleautomation
AT mariepetiot humancenteredaitosupportanadaptivemanagementofhumanmachinetransitionswithvehicleautomation
AT bertrandrichard humancenteredaitosupportanadaptivemanagementofhumanmachinetransitionswithvehicleautomation
AT joshuaquick humancenteredaitosupportanadaptivemanagementofhumanmachinetransitionswithvehicleautomation
_version_ 1724364534905307136