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Browsing by Author "Nadri, Chihab"

Now showing 1 - 7 of 7
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    Development and Evaluation of an Assistive In-Vehicle System for Responding to Anxiety in Smart Vehicles
    Nadri, Chihab (Virginia Tech, 2023-10-18)
    The integration of automated vehicle technology into our transportation infrastructure is ongoing, yet the precise timeline for the introduction of fully automated vehicles remains ambiguous. This technological transition necessitates the creation of in-vehicle displays tailored to emergent user needs and concerns. Notably, driving-induced anxiety, already a concern, is projected to assume greater significance in this context, although it remains inadequately researched. This dissertation sought to delve into the phenomenon of anxiety in driving, assess its implications in future transportation modalities, elucidate design considerations for distinct demographics like the youth and elderly, and design and evaluate an affective in-vehicle system to alleviate anxiety in automated driving through four studies. The first study involved two workshops with automotive experts, who underscored anxiety as pivotal to sustaining trust and system acceptance. The second study was a qualitative focus group analysis incorporating both young and older drivers, aiming to distill anxiety-inducing scenarios in automated driving and pinpoint potential intervention strategies and feedback modalities. This was followed by two driving simulator evaluations. The third study was observational, seeking to discern correlations among personality attributes, anxiety, and trust in automated driving systems. The fourth study employed cognitive reappraisal for anxiety reduction in automated driving. Analysis indicated the efficacy of the empathic interface leveraging cognitive reappraisal as an effective anxiety amelioration tool. Particularly in the self-efficacy reappraisal context, this influence influenced trust, user experience, and anxiety markers. Cumulatively, this dissertation provides key design guidelines for anxiety mitigation in automated driving, and highlights design elements pivotal to augmenting user experiences in scenarios where drivers relinquish vehicular control.
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    Emotion GaRage Vol. IV: Creating Empathic In-Vehicle Interfaces with Generative AIs for Automated Vehicle Contexts
    Choe, Mungyeong; Bosch, Esther; Dong, Jiayuan; Alvarez, Ignacio; Oehl, Michael; Jallais, Christophe; Alsaid, Areen; Nadri, Chihab; Jeon, Myounghoon (ACM, 2023-09-18)
    This workshop aims to design advanced empathic user interfaces for in-vehicle displays, particularly for high-level automated vehicles (SAE level 3 or higher). Incorporating model-based approaches for understanding human emotion regulation, it seeks to enhance the user-vehicle interaction. A unique aspect of this workshop is the integration of generative artificial intelligence (AI) tools in the design process. The workshop will explore generative AI’s potential in crafting contextual responses and its impact on user experience and interface design. The agenda includes brainstorming on various driving scenarios, developing emotion-oriented intervention methods, and rapid prototyping with AI tools. The anticipated outcome includes practical prototypes of affective user interfaces and insights on the role of AI in designing human-machine interactions. Through this workshop, we hope to contribute to making automated driving more accessible and enjoyable.
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    Empathic vehicle design: Use cases and design directions from two workshops
    Nadri, Chihab; Alvarez, Ignacio; Bosch, Esther; Oehl, Michael; Braun, Michael; Healey, Jennifer; Jallais, Christophe; Ju, Wendy; Li, Jingyi; Jeon, Myounghoon (ACM, 2022-04-27)
    Empathic vehicles are expected to improve user experience in automated vehicles and to help increase user acceptance of technology. However, little is known about potential real-world implementations and designs using empathic interfaces in vehicles with higher levels of automation. Given advances in affect detection and emotion mitigation, we conducted two workshops (N1 =24, N2 = 22, Ntotal = 46) on the design of empathic vehicles and their potential utility in a variety of applications. This paper recapitulates key opportunities in the design and application of empathetic interfaces in automated vehicles which emerged from the two workshops hosted at the ACM AutoUI conferences.
  • From Visual Art to Music: Sonification Can Adapt to Painting Styles and Augment User Experience
    Nadri, Chihab; Anaya, Chairunisa; Yuan, Shan; Jeon, Myounghoon (Taylor & Francis, 2022-07-01)
    Advances in the fields of data processing and sonification have been applied to transcribe a variety of visual experiences into an auditory format. Although image sonification examples exist, the application of these principles to visual art has not been examined thoroughly. We sought to develop and evaluate a set of guidelines for the sonification of visual artworks. Through conducting expert interviews (N = 11), we created an initial sonification algorithm that accounts for art style, lightness, and color diversity to modulate the sonified output in terms of tempo and pitch. This algorithm was evaluated through user evaluations (N = 22). User study responses supported expert interview findings, the notion that sonification can be designed to match the experience of viewing an artwork, and showed interesting interaction effects among art styles, visual components, and musical parameters. We suggest the proposed guidelines can augment visitor experiences at art exhibits and provide the basis for further experimentation.
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    Improving Safety At Highway-Rail Grade Crossings Using In-Vehicle Auditory Alerts
    Nadri, Chihab; Lautala, Pasi; Veinott, Elizabeth; Mamun, Tauseef Ibn; Dam, Abhraneil; Jeon, Myounghoon (ACM, 2023-09-18)
    Despite increased use of lights, gates, and other active warning devices, crashes still happen at Highway-Rail Grade Crossings (HRGCs). To improve safety at HRGCs, we designed an in-vehicle auditory alert (IVAA) and conducted a multi-site driving simulator study to evaluate the effect of the IVAA on driving behavior at HRGCs. The video shows results of the collaboration between Virginia Tech, Michigan Tech, and the Volpe National Transportation Center recruited a total of N = 72 younger drivers. Driver simulator testing showed that the IVAA improved driving behavior near HRGCs, improving gaze behavior at HRGCs. Drivers looked both ways at crossings more often when the IVAA was present. We expect to run additional tests to further improve the IVAA. Our study can contribute to research efforts targeting driving safety at HRGCs.
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    "Play Your Anger": A Report on the Empathic In-vehicle Interface Workshop
    Dong, Jiayuan; Nadri, Chihab; Alvarez, Ignacio; Diels, Cyriel; Lee, Myeongkyu; Li, Jingyi; Liao, Pei Hsuan; Manger, Carina; Sadeghian, Shadan; Schuß, Martina; Walker, Bruce N.; Walker, Francesco; Wang, Yiyuan; Jeon, Myounghoon (ACM, 2023-09-18)
    Empathic in-vehicle interfaces are critical in improving user safety and experiences. There has been much research on how to estimate drivers’ affective states, whereas little research has investigated intervention methods that mitigate potential impacts from the driver’s affective states on their driving performance and user experiences. To enhance the development of in-vehicle interfaces considering emotional aspects, we have organized a workshop series to gather automotive user interface experts to discuss this topic at the International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutoUI). The present paper focuses particularly on the intervention methods created by the experts and proposes design recommendations for future empathic in-vehicle interfaces. We hope this work can spark lively discussions on the importance of drivers’ affective states in their user experience of automated vehicles and pose the right direction.
  • "Slow down. Rail crossing ahead. Look left and right at the crossing": In-vehicle auditory alerts improve driver behavior at rail crossings
    Nadri, Chihab; Kekal, Siddhant; Li, Yinjia; Li, Xuan; Lee, Seul Chan; Nelson, David; Lautala, Pasi; Jeon, Myounghoon (Elsevier, 2022-09-27)
    Even though the rail industry has made great strides in reducing accidents at crossings, train-vehicle collisions at Highway-Rail Grade Crossings (HRGCs) continue to be a major issue in the US and across the world. In this research, we conducted a driving simulator study (N = 35) to evaluate a hybrid in-vehicle auditory alert (IVAA), composed of both speech and non-speech components, that was selected after two rounds of subjective evaluation studies. Participants drove through a simulated scenario and reacted to HRGCs with and without the IVAA present and through different music conditions and crossing devices. Driver simulator testing results showed that the inclusion of the hybrid IVAA significantly improved driving behavior near HRGCs in terms of gaze behavior, braking reaction, and approach speed to the crossing. The driving simulator study also showed the effects of background music and warning device types on driving performance. The study contributes to the large-scale implementation of IVAAs at HRGCs, as well as the development of guidelines toward a more standardized approach for IVAAs at HRGCs.