The connected car

Svangren, Michael Kvist; Skov, Mikael B.; Kjeldskov, Jesper

doi:10.1145/3098279.3098535

Cited by 21 publications

(14 citation statements)

References 32 publications

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“…Firstly, studies in related contexts such as autonomous driving, electric vehicles, driver support systems, and connected vehicles have recognized the need for research into factors that determine drivers' acceptance (Adell, 2010;Adnan et al, 2018;Hanesch et al, 2022;C. Lee et al, 2017;Nordhoff et al, 2018;Osswald et al, 2012;Panagiotopoulos & Dimitrakopoulos, 2018;Seeger & Bick, 2013;Souders & Charness, 2016;Svangren et al, 2017;Xu et al, 2018). However, there is only limited research available on determinants of drivers' acceptance of ODCFs (e.g., Garbas et al, 2022;Ma et al, 2015;Schaefers et al, 2022;Wiegand & Imschloss, 2021), but it is crucial to understand adoption criteria to ensure their usage and thereby create additional value for customers (Juehling et al, 2010).…”

Section: Entrepreneurship and Sustainability Issuesmentioning

confidence: 99%

Integrated Acceptance Model for On-demand Car Functions: exploring determinants of drivers' acceptance

Gräsner,

Vogt

2023

JESI

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This research paper investigates the factors influencing drivers' acceptance of on-demand car functions (ODCFs) and proposes an integrated acceptance model specific to the ODCFs context. While limited marketing research has explored consumer responses to ODCFs, understanding the determinants of consumers' intention to accept ODCFs is crucial. Existing acceptance models, although effective in explaining variances in consumer behavior, need to be adapted and extended to enhance explanatory power in individual contexts. To address this gap, a comprehensive literature review on ODCFs and related domains was conducted, identifying 74 acceptance factors. Drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT), the Car Technology Acceptance Model (CTAM), and the identified factors, a multi-level acceptance model tailored to the ODCFs context was developed. At the meso-level, the baseline model incorporates factors such as exposure to ODCFs, domain-specific, symbolic-affective, and moral-normative factors. The micro-level pertains to distinct individual variance components, encompassing socio-demographic attributes, travel behavioral patterns, personality dispositions, and technological inclinations. These micro-level determinants exert a discernible influence on the factors situated at the meso-level of analysis. A partial model that considers cross-level influences and advocates for multi-level research to examine the contextual factors' impacts on acceptance empirically is proposed to operationalize the model. By adopting this approach, researchers can gain deeper insights into the acceptance of ODCFs and shed light on the mechanisms underlying consumer behavior in this specific context.

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Section: Entrepreneurship and Sustainability Issuesmentioning

confidence: 99%

Integrated Acceptance Model for On-demand Car Functions: exploring determinants of drivers' acceptance

Gräsner,

Vogt

2023

JESI

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“…These IoV apps form an interactive conduit between users and their vehicles via mobile devices, bestowing a multitude of control features and informational services. For instance, through remote control functionalities, users can initiate or shut down their vehicle, lock or unlock doors, and even modify certain settings such as seat adjustments and air conditioning, under certain conditions [4,5]. Companies such as Tesla and Volkswagen equip their latest vehicles with these IoV apps extensively, thereby enhancing the convenience of user-vehicle interactions [6].…”

Section: Introductionmentioning

confidence: 99%

A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

Zhang

Sun

2023

Electronics

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In the non-intrusive automated testing system for Internet of Vehicles (IoV) applications, automatic recognition of text and icons on vehicle central control screens is of paramount importance. However, the detection and recognition of content on vehicle central control screens are inherently complex. Additionally, during non-intrusive vehicle central control screen image testing, there is a deficiency of suitable datasets and detection methods. This deficiency renders information within vehicle application images difficult to be accurately extracted by the detection network. To address this problem, this study first constructs a dataset tailored for text detection and recognition on vehicle screens. This dataset encompasses a variety of vehicle central control images, enabling the generic text detection and recognition network to more effectively identify and interpret text within vehicle screens. Subsequently, this research proposes an enhanced Fully Convolutional Networks for Text Detection (FOTS) method for vehicle central control screen text detection and recognition. This method elevates the semantic expression capabilities of features by sharing vehicle central control screen text detection and recognition features. Furthermore, it improves multi-scale feature processing capabilities through the utilization of a feature transformation module. Validation through visual and quantitative experiments demonstrates that the proposed method can effectively accomplish text detection and recognition tasks on vehicle screens. This achievement bears significant implications for the field of automated testing in IoV applications.

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“…Since few people have experienced everyday life with autonomous cars, UX-related research to date has focused on understanding individual users' relations to the technology in experimental conditions, and people's attitudes towards AD cars [1]. However, the question of how AD cars and related services are experienced and integrated into people's everyday lives has been identified as requiring further research [3][4][5], in order to address future scenarios at early design stages [6] and develop a better understanding of alternative preferable and desirable technology and service design concepts. In this paper, we outline what can be learned about people's situated experiences and adoption of AD by using the concept of anticipation to investigate how people gradually learn AD in their everyday commuting contexts while engaging with already existing and evolving ADAS technologies, e.g.…”

Section: Introductionmentioning

confidence: 99%

Anticipatory experience in everyday autonomous driving

Lindgren

Fors

Pink

et al. 2020

Pers Ubiquit Comput

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In this paper, we discuss how people’s user experience (UX) of autonomous driving (AD) cars can be understood as a shifting anticipatory experience, as people experience degrees of AD through evolving advanced driver assistance systems (ADAS) in their everyday context. We draw on our ethnographic studies of five families, who had access to AD research cars with evolving ADAS features in their everyday lives for a duration of 1½ years. Our analysis shows that people gradually adopt AD cars, through a process that involves anticipating if they can trust them, what the ADAS features will do and what the longer-term technological possibilities will be. It also showed that this anticipatory UX occurs within specific socio-technical and environmental circumstances, which could not be captured easily in experimental settings. The implication is that studying anticipation offers us new insights into how people adopt AD in their everyday commute driving.

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The connected car

Cited by 21 publications

References 32 publications

Integrated Acceptance Model for On-demand Car Functions: exploring determinants of drivers' acceptance

Integrated Acceptance Model for On-demand Car Functions: exploring determinants of drivers' acceptance

A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

Anticipatory experience in everyday autonomous driving

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