Adaptive User Experience in the Car—Levels of Adaptivity and Adaptive HMI Design

Rittger, Lena; Engelhardt, Doreen; Schwartz, Robert

doi:10.1109/tits.2021.3124990

Cited by 6 publications

(6 citation statements)

References 27 publications

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“…The emphasis on 'switching' implies users' desires for effortlessness through flexible, adaptable, alterable and versatile experiences within a car space. This aligns with the studies that show the importance of adapting in-car systems in accordance with individual user requirements and contexts through flexing the in-car space and interactions (Krome et al 2015;Rittger et al 2022). Users reflect their needs into context, and context provides the right assistance in terms of how information is conveyed and how systems respond (Rittger et al 2022).…”

Section: Discussionsupporting

confidence: 84%

When do People Expect Effortless In-Car Interactions?

Ince¹,

Cha²,

Cho³

2023

Proceedings of the 6th International Conference on Intelligent Human Systems Integration (IHSI 2023) Integrating People and Int

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Drivers have experienced various in-car interactions due to advanced infotainment systems and digital integration in cars. The scope of in-car interaction is likely to be further expanded in autonomous driving due to the increased free time in the car for focusing on non-driving activities. When designing in-car interactions, enhancing the in-car user experience by giving drivers new abilities and providing them with effortless and intuitive interactions is a worthy goal. Hence, understanding users’ perspectives in the early phases is the critical first step to informing the design process. Although prior studies have revealed users’ expectations and needs in an autonomous vehicle, there is a lack of understanding of when and in which context users might most desire effortless interaction. This investigation aims to examine users’ expectations and identify themes for effortless in-car interaction. One hundred fifty participants were recruited using a purposive sampling strategy. The study consists of an open-ended online questionnaire that enquired about the context in which people desire effortless interaction within a car the most. Questionnaire responses were clustered into themes using a thematic analysis method. The study proposes a taxonomy of in-car contexts composed of six major themes, with 17 sub-themes, which include the following contexts: 1) switching-required, 2) emotion-underlain, 3) idle-away, 4) less-controllable, 5) time-sensitive and 6) task-oriented. The findings provide guidance regarding the critical contexts of effortless interaction, which designers can use to better understand and improve automotive experiences.

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Section: Discussionsupporting

confidence: 84%

When do People Expect Effortless In-Car Interactions?

Ince¹,

Cha²,

Cho³

2023

Proceedings of the 6th International Conference on Intelligent Human Systems Integration (IHSI 2023) Integrating People and Int

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“…One of the key accomplishments of A-HMI lies in its significant contribution to reducing the workload and distractions faced by drivers by effectively addressing the challenges posed by the abundant and diverse information generated by In-Vehicle Information Systems (IVIS), Advanced Driver Assistance Systems (ADAS) [21], and nomadic devices [22]. Indeed, except for fully AVs, drivers are still expected to uphold attention towards the driving environment and be prepared to assume control of the driving task if the situation demands [23].…”

Section: A-hmis As a Solution To Reduce Driver Distractionmentioning

confidence: 99%

“…In addition to adaptation strategies, A-HMIs can also be categorized into several Levels of Adaptive Sensitive Responses (LASR) [22]. The LASR framework outlines 5 levels of adaptivity, from a level 0, where no adaptivity is present, to a level 4, representing systems that can make connections between system behavior, context, user reactions, and even infer mood, emotional state, or personality [38].…”

Section: Related Workmentioning

confidence: 99%

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An Optimization Framework for Information Management in Adaptive Automotive Human–Machine Interfaces

Tufano,

Bahadure,

Tufo

et al. 2023

Applied Sciences

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In recent years, advancements in Intelligent and Connected Vehicles (ICVs) have led to a significant increase in the amount of information to the driver through Human–Machine Interfaces (HMIs). To prevent driver cognitive overload, the development of Adaptive HMIs (A-HMIs) has emerged. Indeed, A-HMIs regulate information flows by dynamically adapting the presentation to suit the contextual driving conditions. This paper presents a novel methodology, based on multi-objective optimization, that offers a more generalized design approach for adaptive strategies in A-HMIs. The proposed methodology is specifically tailored for designing an A-HMI that, by continuously monitoring the Driver–Vehicle–Environment (DVE) system, schedules actions requested by applications and selects appropriate presentation modalities to suit the current state of the DVE. The problem to derive these adaptive strategies is formulated as an optimization task where the objective is to find a set of rules to manage information flow between vehicle and driver that minimizes both the driver’s workload and the queuing of actions. To achieve these goals, the methodology evaluates through two indexes how applications’ requests impact the driver’s cognitive load and the waiting queue for actions. The optimization procedure has been solved offline to define adaptive strategies for scheduling five application requests, i.e., forward collision warning, system interaction, turn indicators, infotainment volume increase, and phone calls. A theoretical analysis has demonstrated the effectiveness of the proposed framework in optimizing the prioritization strategy for actions requested by applications. By adopting this approach, the design of rules for the scheduling process of the A-HMI architecture is significantly streamlined while gaining adaptive capabilities to prevent driver cognitive overload.

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“…, 2022; Xiao and Greer, 2007). Given that IT artifacts have low material agency to drive themselves to autonomously serve the goals that fit needs by continuously learning and adapting without human intervention, material adaptivity is limited to a low level or may even be nonexistent (Rittger et al. , 2022).…”

Section: Introductionmentioning

confidence: 99%

How does material adaptivity of smart objects shape infusion use? The pivot role of social embeddedness

Pan

et al. 2023

INTR

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PurposeAdvances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.Design/methodology/approachThis study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.FindingsThe results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.Practical implicationsThe formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.Originality/valueThis study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

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Adaptive User Experience in the Car—Levels of Adaptivity and Adaptive HMI Design

Cited by 6 publications

References 27 publications

When do People Expect Effortless In-Car Interactions?

When do People Expect Effortless In-Car Interactions?

An Optimization Framework for Information Management in Adaptive Automotive Human–Machine Interfaces

How does material adaptivity of smart objects shape infusion use? The pivot role of social embeddedness

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