Benefits of Personalization in the Context of a Speech-Based Left-Turn Assistant

Orth, Dennis; Schömig, Nadja; Mark, Christian; Jagiellowicz-Kaufmann, Monika; Kolossa, Dorothea; Heckmann, Martin

doi:10.1145/3122986.3123004

Cited by 16 publications

(4 citation statements)

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“…These range from knowing the user's name, to content customizations based on models of needs and behaviors [19,49]. Such systems can also act as social players by proactively pointing out information [36,47] or by helping users to accept new technologies, e.g., by mimicking their behavior in automated driving [37]. Personalization can also help maintaining attachment to cars when ownership and driving are things of the past [7].…”

Section: Personalization Of User Interfacesmentioning

confidence: 99%

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Braun

Mainz

Chadowitz

et al. 2019

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

138

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Section: Personalization Of User Interfacesmentioning

confidence: 99%

“…H2: A personalized character scores higher ratings for trust and user experience, compared to the default assistant (cf. [37]).…”

Section: Hypothesesmentioning

confidence: 99%

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Braun

Mainz

Chadowitz

et al. 2019

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

138

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“…We saw in the previous section that different drivers chose different gaps in traffic. This motivated us to carry out a third study, investigating whether a personalization of the gap suggestions of the AOD system to the individual driver will further increase its acceptance (20), (21) .…”

Section: User Acceptance Of Personalized Aodmentioning

confidence: 99%

Development of a Cooperative On-Demand Intersection Assistant

Heckmann

Wersing

Orth

et al. 2019

IJAE

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In this paper, we present our recently introduced "assistance on demand (AOD)" concept, which allows the driver to request assistance via speech whenever he or she deems it appropriate. The target scenario we currently investigate is turning left from a subordinate road in dense urban traffic. We first compare our system in a driving simulator study to driving without assistance or with visual assistance. The results show that drivers clearly prefer our speech-based AOD approach. Next, we investigate the differences between drivers' left-turn behaviour in a driving simulator. The results of this investigation show that there are large inter-individual differences. Based on these results, we present another driving simulator study, where participants can compare manual driving to driving with a default and a personalized AOD system. The results of this second study show that the personalization very notably improves the acceptance of the system. Given the choice between driving with any of the AOD variants and manual driving, 87.5% of the participants preferred driving with the AOD. Finally, we present an evaluation of the AOD system in a prototype vehicle in real urban traffic.

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Identifying Personality Dimensions for Characters of Digital Agents

Braun

Alt

2020

Human–Computer Interaction Series

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Benefits of Personalization in the Context of a Speech-Based Left-Turn Assistant

Cited by 16 publications

References 5 publications

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Development of a Cooperative On-Demand Intersection Assistant

Identifying Personality Dimensions for Characters of Digital Agents

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