How to Increase Automated Vehicles’ Acceptance through In-Vehicle Interaction Design: A Review

Detjen, Henrik; Faltaous, Sarah; Pfleging, Bastian; Geisler, Stefan; Schneegaß, Stefan

doi:10.1080/10447318.2020.1860517

Cited by 79 publications

(37 citation statements)

References 172 publications

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“…Recognition of driver's mental state is key enabler for increasing safety during assisted driving. [9] 2.…”

Section: Full Automationmentioning

confidence: 99%

“…A few other articles have reviewed the interior designs of AVs to better support and interact with drivers and passengers. [8,9] Likewise, the user interfaces (UIs) and user experience (UX) of vehicle interiors have been studied in context with manual as well as automated driving. [10] A number of previous works have also studied the interaction with the external…”

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confidence: 99%

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Intelligent In‐Vehicle Interaction Technologies

Murali

Kaboli

Dahiya

2021

Advanced Intelligent Systems

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Autonomous vehicles (AVs) will soon become a reality with almost every major automotive original equipment manufacturer (OEM) such as Tesla, BMW, Daimler, and big technology giants such as Google's Waymo and Apple Car pushing toward the full self-driving objective. According to the SAE J3016 taxonomy [1] of autonomous driving, there are six levels of automated driving systems ranging from level 0 (completely manual) to level 5 (full selfdriving [FSD]) automated systems that are expected to function in all geographic locations, all weather conditions, and under all conditions. The proposed benefits of intelligent vehicles include fewer road accidents, enhanced safety, reduced traffic congestion, effective commute time usage, and importantly enjoyable and comfortable ride. With increased autonomy, the drivers also assume the role of mere passengers who are engaged in nondriving activities and are unavailable to participate in traffic interactions. This would increase complexity in a mixed-autonomy traffic environment as interactions with pedestrians and cyclists are based on visual cues with the driver. Therefore, intelligent vehicles also need to autonomously interact with other traffic participants such as pedestrians and cyclists and other vehicles.Human-vehicle interaction (HVI) is closely related to the field of human-robot interaction (HRI). It entails the problem of understanding and shaping the interaction dynamics between humans and vehicles. Particularly, the domain of interaction involves the study of sensation, perception, information exchange, inference, and decision-making. [2] With increasing levels of automation, the driver will have more time and choice to perform various tasks other than driving and this opens new avenues for interaction. As a result, a growing number of sensors are being incorporated into the vehicles to understand the driver's and/or passenger's actions, emotions, and personal choices to offer the precise functionalities and services for an enjoyable ride. [3] Figure 1 shows the schematic of in-vehicle interaction consisting of various interactive interfaces and sensing modalities present in the vehicle as well as some modes of interaction such as gestures, speech, and eye gaze.The advances in HVI have been presented in previous review articles, which have mainly focused on the detection of particular characteristics for driving assistance such as driver attention detection, [4] emotion recognition, [5] drowsiness detection, [6] mental workload, [7] and so on. A few other articles have reviewed the interior designs of AVs to better support and interact with drivers and passengers. [8,9] Likewise, the user interfaces (UIs) and user experience (UX) of vehicle interiors have been studied in context with manual as well as automated driving. [10] A number of previous works have also studied the interaction with the external

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“…Recognition of driver's mental state is key enabler for increasing safety during assisted driving. [9] 2.…”

Section: Full Automationmentioning

confidence: 99%

mentioning

confidence: 99%

Intelligent In‐Vehicle Interaction Technologies

Murali

Kaboli

Dahiya

2021

Advanced Intelligent Systems

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“…A nationwide survey of 2,167 Americans indicated that 58.4% were afraid to ride in an AV and only 19.5% would be comfortable riding in an AV (Bansal & Kockelman, 2017). Because public opinion ultimately plays a crucial role in the adoption of AVs, understanding the factors impacting people's opinions is extremely important (Azevedo-Sa et al, 2020;Detjen et al, 2021;Du et al, 2020;Favarò et al, 2019;Ricci, n.d.).…”

Section: Introductionmentioning

confidence: 99%

Individual Differences and Expectations of Automated Vehicles

Zhang

Yang

Robert

2021

International Journal of Human–Computer Interaction

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Individual Differences and Expectations of Automated VehiclesDespite the benefits of automated vehicles (AVs), there are still barriers to their widespread adoption. Expectations about AVs have been identified as one of the most important factors in understanding AV adoption. Therefore, by understanding the public's expectations of AVs, we can better understand whether or when AVs are likely to be adopted on a wide scale.Individual differences, including demographics and personality, have been identified as factors that impact technology expectations and adoption. However, it is not clear whether and how individual differences can influence expectations of AVs. To examine this, we conducted an online survey with 443 U.S. drivers who were recruited and divided into subpopulations by age, gender, ethnicity, census region, educational level, marital status, income, driving frequency, driving experience, and personality traits. Results revealed that drivers' expectations of AVs differ significantly by age, gender, ethnicity, education levels, marital status, drive frequency, drive experience, and personality. More specifically, higher expectations are more often generated by drivers who are younger, men, White non-Hispanic, more highly educated, never married, with a higher frequency of driving, with less driving experience, and who are high in extraversion, agreeableness, conscientiousness, and emotional stability. The results of this study provide a foundation for future research related to expectations and have important implications on future design and development of AVs.

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“…Concerning (3a), preknowledge, ease of learning, and self-rated knowledge increase perceived trust [14]. User Onboarding might help to calibrate trust to an adequate level [15]. E.g., Tenhundfeld et al found that a better understanding of the Tesla "Autopark"-assistant leads to higher trust ratings [16].…”

Section: Effects Of User Onboarding On Experience With Automated Vehiclesmentioning

confidence: 99%

Supporting User Onboarding in Automated Vehicles through Multimodal Augmented Reality Tutorials

Detjen

Degenhart

Schneegaß

et al. 2021

MTI

Self Cite

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Misconceptions of vehicle automation functionalities lead to either non-use or dangerous misuse of assistant systems, harming the users’ experience by reducing potential comfort or compromise safety. Thus, users must understand how and when to use an assistant system. In a preliminary online survey, we examined the use, trust, and the perceived understanding of modern vehicle assistant systems. Despite remaining incomprehensibility (36–64%), experienced misunderstandings (up to 9%), and the need for training (around 30%), users reported high trust in the systems. In the following study with first-time users, we examine the effect of different User Onboarding approaches for an automated parking assistant system in a Tesla and compare the traditional text-based manual with a multimodal augmented reality (AR) smartphone application in means of user acceptance, UX, trust, understanding, and task performance. While the User Onboarding experience for both approaches shows high pragmatic quality, the hedonic quality was perceived significantly higher in AR. For the automated parking process, reported hedonic and pragmatic user experience, trust, automation understanding, and acceptance do not differ, yet the observed task performance was higher in the AR condition. Overall, AR might help motivate proper User Onboarding and better communicate how to operate the system for inexperienced users.

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How to Increase Automated Vehicles’ Acceptance through In-Vehicle Interaction Design: A Review

Cited by 79 publications

References 172 publications

Intelligent In‐Vehicle Interaction Technologies

Intelligent In‐Vehicle Interaction Technologies

Individual Differences and Expectations of Automated Vehicles

Supporting User Onboarding in Automated Vehicles through Multimodal Augmented Reality Tutorials

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