Driver in the Loop: Best Practices in Automotive Sensing and Feedback Mechanisms

Riener, Andreas; Jeon, Myounghoon; Álvarez, Ignacio; Frison, Anna Katharina

doi:10.1007/978-3-319-49448-7_11

Cited by 30 publications

(19 citation statements)

References 33 publications

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“…Each of these strategies incorporates a driver-vehicle interaction loop, meaning the system senses driver data, estimates an emotional state, and reacts accordingly [34]. Different modalities with varying degrees of blatancy are used to influence the driver towards an optimal driving state, which harbours the risk of introducing side effects such as distraction or perceived paternalism [53].…”

Section: Discussionmentioning

confidence: 99%

“…Williams et al explored another facet of emotional interaction in the car with a social robot, which also had positive effects on driving performance [33]. We take this aspect of adaptive and social interaction and connect it to an affect-integrated driver model in order to intervene within the driver-vehicle interaction loop [1,16,34]. Figure 1 shows the driver state taxonomy used in many related studies, which defines dangerous states with extreme values of arousal in combination with negative valence (e.g., anger and sadness) and identifies medium arousal and positive valence as optimal driving state.…”

Section: Designing Affective In-car Assistantsmentioning

confidence: 99%

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Improving Driver Emotions with Affective Strategies

Braun

Schubert

Pfleging

et al. 2019

MTI

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Drivers in negative emotional states, such as anger or sadness, are prone to perform bad at driving, decreasing overall road safety for all road users. Recent advances in affective computing, however, allow for the detection of such states and give us tools to tackle the connected problems within automotive user interfaces. We see potential in building a system which reacts upon possibly dangerous driver states and influences the driver in order to drive more safely. We compare different interaction approaches for an affective automotive interface, namely Ambient Light, Visual Notification, a Voice Assistant, and an Empathic Assistant. Results of a simulator study with 60 participants (30 each with induced sadness/anger) indicate that an emotional voice assistant with the ability to empathize with the user is the most promising approach as it improves negative states best and is rated most positively. Qualitative data also shows that users prefer an empathic assistant but also resent potential paternalism. This leads us to suggest that digital assistants are a valuable platform to improve driver emotions in automotive environments and thereby enable safer driving.

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Section: Discussionmentioning

confidence: 99%

Section: Designing Affective In-car Assistantsmentioning

confidence: 99%

Improving Driver Emotions with Affective Strategies

Braun

Schubert

Pfleging

et al. 2019

MTI

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show abstract

“…Voice interfaces have been shown as valuable alternative input modalities for automotive user interfaces [40,44,45]. Drivers mainly utilize visual and manual cognitive resources for the driving task, without extensively straining vocal and auditory channels [52].…”

Section: Voice Assistants In the Carmentioning

confidence: 99%

“…Voice assistants are becoming a pervasive means of interaction in everyday life [41]. A similar trend is apparent for automotive UIs [44]. Apart from minimizing driver distraction during manual driving [27,39], speech interfaces also offer a more natural user experience (UX), compared to conventional UIs in cars [1], which is of particular interest in the transition towards automated driving.…”

Section: Introductionmentioning

confidence: 99%

At Your Service

Braun

Mainz

Chadowitz

et al. 2019

Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

116

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“…To that end, EDA sensors could be integrated into steering wheels and measure changes in the conductivity of the skin. Such configurations have previously been touched upon by Meschtscherjakov (2017) and Riener et al (2017) . Future work should explore further dimensions of driver states and boredom levels.…”

Section: Resultsmentioning

confidence: 94%

Risky Gadgets to the Rescue - Reframing In-Car Technology Use as Task Engagement

Steinberger¹

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Driver in the Loop: Best Practices in Automotive Sensing and Feedback Mechanisms

Cited by 30 publications

References 33 publications

Improving Driver Emotions with Affective Strategies

Improving Driver Emotions with Affective Strategies

At Your Service

Risky Gadgets to the Rescue - Reframing In-Car Technology Use as Task Engagement

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