Analysis of autonomic indexes on drivers' workload to assess the effect of visual ADAS on user experience and driving performance in different driving conditions

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dc.contributor.authorAriansyah, Dedy
dc.contributor.authorCaruso, Giandomenico
dc.contributor.authorRuscio, Daniele
dc.contributor.authorBordegoni, Monica
dc.date.accessioned2019-09-16T08:46:05Z
dc.date.available2019-09-16T08:46:05Z
dc.date.issued2018-06-12
dc.description.abstractAdvanced driver assistance systems (ADASs) allow information provision through visual, auditory, and haptic signals to achieve multidimensional goals of mobility. However, processing information from ADAS requires operating expenses of mental workload that drivers incur from their limited attentional resources. The change in driving condition can modulate drivers' workload and potentially impair drivers' interaction with ADAS. This paper shows how the measure of cardiac activity (heart rate and the indexes of autonomic nervous system (ANS)) could discriminate the influence of different driving conditions on drivers' workload associated with attentional resources engaged while driving with ADAS. Fourteen drivers performed a car-following task with visual ADAS in a simulated driving. Drivers' workload was manipulated in two driving conditions: one in monotonous condition (constant speed) and another in more active condition (variable speed). Results showed that drivers' workload was similarly affected, but the amount of attentional resources allocation was slightly distinct between both conditions. The analysis of main effect of time demonstrated that drivers' workload increased over time without the alterations in autonomic indexes regardless of driving condition. However, the main effect of driving condition produced a higher level of sympathetic activation on variable speed driving compared to driving with constant speed. Variable speed driving requires more adjustment of steering wheel movement (SWM) to maintain lane-keeping performance, which led to higher level of task involvement and increased task engagement. The proposed measures appear promising to help designing new adaptive working modalities for ADAS on the account of variation in driving condition.en_UK
dc.identifier.citationAriansyah D, Caruso G, Ruscio D, Bordegoni M. Analysis of autonomic indexes on drivers' workload to assess the effect of visual ADAS on user experience and driving performance in different driving conditions. Journal of Computing and Information Science in Engineering, Volume 18, Issue 3, September 2018, Article number 031007en_UK
dc.identifier.issn1530-9827
dc.identifier.urihttps://doi.org/10.1115/1.4039313
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14535
dc.language.isoenen_UK
dc.publisherASMEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectHuman computer interfacesen_UK
dc.subjectVirtual and augmented reality environmentsen_UK
dc.subjectVirtual Prototypingen_UK
dc.subjectHuman computer interactionsen_UK
dc.titleAnalysis of autonomic indexes on drivers' workload to assess the effect of visual ADAS on user experience and driving performance in different driving conditionsen_UK
dc.typeArticleen_UK

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