In-Vehicle Interface Adaptation to Environment-Induced Cognitive Workload

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In-Vehicle Interface Adaptation to Environment-Induced

Cognitive Workload

Elena Meiser

German Research Center for Articial

Intelligence (DFKI)

Saarbrücken, Germany

elena.meiser@dfki.de

Alexandra Alles

German Research Center for Articial

Intelligence (DFKI)

Saarbrücken, Germany

alexandra_katrin.alles@dfki.de

Samuel Selter

German Research Center for Articial

Intelligence (DFKI)

Saarbrücken, Germany

samuel.selter@dfki.de

Marco Molz

German Research Center for Articial

Intelligence (DFKI)

Saarbrücken, Germany

marco.molz@dfki.de

Amr Gomaa

German Research Center for Articial

Intelligence (DFKI)

Saarbrücken, Germany

Saarland Informatics Campus

Saarbrücken, Germany

amr.gomaa@dfki.de

Guillermo Reyes

German Research Center for Articial

Intelligence (DFKI)

Saarbrücken, Germany

Saarland Informatics Campus

Saarbrücken, Germany

guillermo.reyes@dfki.de

ABSTRACT

Many car accidents are caused by human distractions, including

cognitive distractions. In-vehicle human-machine interfaces (HMIs)

have evolved throughout the years, providing more and more func-

tions. Interaction with the HMIs can, however, also lead to further

distractions and, as a consequence, accidents. To tackle this prob-

lem, we propose using adaptive HMIs that change according to

the mental workload of the driver. In this work, we present the

current status as well as preliminary results of a user study using

naturalistic secondary tasks while driving (i.e., the primary task)

that attempt to understand the eects of one such interface.

CCS CONCEPTS

•Human-centered computing →User centered design

;

•Ap-

plied computing →Psychology.

KEYWORDS

Psychophysiological Measurements; User Experience; Adaptive

Interfaces; Mental Workload; Multimodal Interaction

ACM Reference Format:

Elena Meiser, Alexandra Alles, Samuel Selter, Marco Molz, Amr Gomaa,

and Guillermo Reyes. 2022. In-Vehicle Interface Adaptation to Environment-

Induced Cognitive Workload. In 14th International Conference on Automotive

User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’22 Ad-

junct), September 17–20, 2022, Seoul, Republic of Korea. ACM, New York, NY,

USA, 4 pages. https://doi.org/10.1145/3544999.3552533

1 INTRODUCTION

Driving is one of the most complex everyday tasks, involving sev-

eral visual and auditory mental subtasks [

]. Driving environment

AutomotiveUI ’22 Adjunct, September 17–20, 2022, Seoul, Republic of Korea

This is the author’s version of the work. It is posted here for your personal use.

Not for redistribution. The denitive Version of Record was published in 14th In-

ternational Conference on Automotive User Interfaces and Interactive Vehicular Appli-

cations (AutomotiveUI ’22 Adjunct), September 17–20, 2022, Seoul, Republic of Korea,

https://doi.org/10.1145/3544999.3552533.

complexity, in terms of visual complexity and vehicle control di-

culty, aect mental workload (MWL) [

]. MWL is an interaction

of task demands, other environmental factors, and human charac-

teristics [

] and refers to the fact, that drivers need to expend

mental eort to maintain a safe driving behavior [

]. High MWL, in

particular overload, is associated with more driving mistakes and

trac accidents [

]. Besides decrements in performance, MWL

can also be assessed by physiological measurements like heart rate

(HR) and heart rate variability (HRV) [

]. Those can be easily

acquired while driving with non-invasive tools and can be used to

classify the current MWL level [

]. In the last few decades, in-

vehicle human-machine interfaces (HMI) have become increasingly

advanced. They generally have a great impact on the relationship

between the driving task and MWL [

]. On one hand, they can

add comfort to the driving task or even aid in its execution, e.g.

giving directions or warnings [

]. On the other hand, they can

also be a source of distraction or additional task load, e.g., giving

too much information [

]. In the rst case, HMIs can help to re-

duce the MWL of the driver while in the latter case they add MWL,

which could lead to driving mistakes or even accidents. Adapting

the HMI to the current MWL of the driver or the diculty of the

driving environment could help reduce the MWL and prevent a

mental overload [

]. This can be implemented by reducing the

information available on the display with rising MWL [

]. Since

this information reduction equals a change on the display, it could

also be possible that drivers nd that change itself distracting or

irritating. In this study, we wanted to test if adapting the HMI to a

dicult driving environment reduces MWL compared to a static

display that keeps the available information constant. We measured

MWL physiologically via HR and HRV as well as behaviorally via

driving performance. Additionally, we assessed secondary task per-

formance by letting participants interact with the HMI while driv-

ing. Since user experience (UX) of in-vehicle HMIs is an important

aspect, we collected UX data via a questionnaire.

arXiv:2210.11271v1 [cs.HC] 20 Oct 2022

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In-VehicleInterfaceAdaptationtoEnvironment-InducedCognitiveWorkloadElenaMeiserGermanResearchCenterforArtificialIntelligence(DFKI)Saarbrücken,Germanyelena.meiser@dfki.deAlexandraAllesGermanResearchCenterforArtificialIntelligence(DFKI)Saarbrücken,Germanyalexandra_katrin.alles@dfki.deSamuelSelterGerman...

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