The Effect of Vehicle Automation Styles on Drivers’ Emotional State

Alsaid, Areen; Lee, John D.; Noejovich, Sofia I.; Chehade, Abdallah

doi:10.1109/tits.2023.3239880

Cited by 10 publications

(6 citation statements)

References 77 publications

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“…For instance, in Figure 8 we can see the proximity of words like “degree,” which is commonly used in trust questionnaires, to words like “postgraduate” which is irrelevant to trust. Future work might explore a mixed-initiative approach to lexicon development leveraging machine learning efficiency and human expertise ( Alsaid and Lee, 2022 ; Alsaid et al, 2023 ). This synergy can provide more robust and accurate results in various natural language processing tasks.…”

Section: Discussionmentioning

confidence: 99%

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Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires

Alsaid,

Li,

Chiou

et al. 2023

Front. Psychol.

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IntroductionTrust has emerged as a prevalent construct to describe relationships between people and between people and technology in myriad domains. Across disciplines, researchers have relied on many different questionnaires to measure trust. The degree to which these questionnaires differ has not been systematically explored. In this paper, we use a word-embedding text analysis technique to identify the differences and common themes across the most used trust questionnaires and provide guidelines for questionnaire selection.MethodsA review was conducted to identify the existing trust questionnaires. In total, we included 46 trust questionnaires from three main domains (i.e., Automation, Humans, and E-commerce) with a total of 626 items measuring different trust layers (i.e., Dispositional, Learned, and Situational). Next, we encoded the words within each questionnaire using GloVe word embeddings and computed the embedding for each questionnaire item, and for each questionnaire. We reduced the dimensionality of the resulting dataset using UMAP to visualize these embeddings in scatterplots and implemented the visualization in a web app for interactive exploration of the questionnaires (https://areen.shinyapps.io/Trust_explorer/).ResultsAt the word level, the semantic space serves to produce a lexicon of trust-related words. At the item and questionnaire level, the analysis provided recommendation on questionnaire selection based on the dispersion of questionnaires’ items and at the domain and layer composition of each questionnaire. Along with the web app, the results help explore the semantic space of trust questionnaires and guide the questionnaire selection process.DiscussionThe results provide a novel means to compare and select trust questionnaires and to glean insights about trust from spoken dialog or written comments.

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Section: Discussionmentioning

confidence: 99%

“…Green, orange, and purple represent items in automation, e-commerce, and human-human trust questionnaires, respectively. and Lee, 2022;Alsaid et al, 2023). This synergy can provide more robust and accurate results in various natural language processing tasks.…”

Section: Word-level Analysis: Common Words and Trust Lexiconmentioning

confidence: 99%

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires

Alsaid,

Li,

Chiou

et al. 2023

Front. Psychol.

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“…Most existing research is focused on making these algorithms more reliable, whereas adaptation to drivers' preferences receives little attention [72]. Careful consideration needs to be given to the question of how and to which extent emotion is integrated into the driving style [73]. Otherwise, passengers might feel uneasy because the vehicle moves in a way that seems robotic and unnatural [74].…”

Section: A Driving Style Adaptationmentioning

confidence: 99%

Audiovisual Affect Recognition for Autonomous Vehicles: Applications and Future Agendas

Karas,

Schuller,

Schuller

2024

IEEE Trans. Intell. Transport. Syst.

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Emotion and a broader range of affective and cognitive states play an important role on the road. While this has been predominantly investigated in terms of driver safety, the approaching advent of autonomous vehicles (AVs) is expected to bring a fundamental shift in focus for emotion recognition in the car, from the driver to the passengers. This work presents a number of affect-enabled applications, including adapting the driving style for an emotional experience or tailoring the infotainment to personal preferences. It attempts to foresee upcoming challenges and provides suggestions for multimodal affect modelling, with a focus on the audio and visual modalities. In particular, this includes context awareness, reliable diarisation of multiple passengers, group affect, and personalisation. Finally, we provide some recommendations on future research directions, including explainability, privacy, and holistic modelling.

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“…Firstly, in educational technology, AC can be utilized to monitor learners' emotional states (Alsaid et al, 2023). It can also provide personalized emotion feedback and support to learners (Blazejowska et al, 2023), thereby helping learners adjust their learning experiences in a personalized manner to enhance learning outcomes.…”

Section: Implications For Education Technologymentioning

confidence: 99%

Affective Computing Research Progress and Its Implications for Education Technology: A Bibliometric Analysis Based on Web of Science via VOSviewer

Jiang,

Huang

2023

Atlantis Highlights in Computer Sciences

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Affective Computing (AC) is a dynamic and evolving research field. This paper presents a bibliometric analysis of 1428 publications related to affective computing, extracted from the Web of Science database, using the VOSviewer software. Through an examination of the existing literature, the study investigates the quantity of AC publications, research countries, important institutions, and leading authors. Co-citation analysis reveals that IEEE Transactions on Affective Computing is the most influential source of literature. Keyword co-occurrence and clustering analysis identify five main research directions in AC: emotion recognition, physiological signal, human-computer interaction, deep learning, and electroencephalography. Lastly, the paper provides relevant recommendations for AC research in educational technology, focusing on personalized learning experiences, affective feedback, emotion recognition, and affective robots.

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The Effect of Vehicle Automation Styles on Drivers’ Emotional State

Cited by 10 publications

References 77 publications

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires

Measuring trust: a text analysis approach to compare, contrast, and select trust questionnaires

Audiovisual Affect Recognition for Autonomous Vehicles: Applications and Future Agendas

Affective Computing Research Progress and Its Implications for Education Technology: A Bibliometric Analysis Based on Web of Science via VOSviewer

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