2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE), Journal Year: 2023, Volume and Issue: unknown, P. 237 - 241
Published: Oct. 28, 2023
Language: Английский
Libri, Journal Year: 2024, Volume and Issue: unknown
Published: Oct. 30, 2024
Abstract Factors influencing Chinese graduate students’ adoption of AI-generated content (AIGC) tools are examined through partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The developed AIGCT-SI model incorporates key elements such as information accuracy, trust, privacy concerns. PLS-SEM results indicate that performance expectancy, effort facilitating conditions, habit significantly impact intentions, with trust acting a mediator, particularly for concerns social influence. FsQCA reveals seven configurations, demonstrating how combinations conditions drive adoption. A bidirectional relationship between is observed, mitigating risks in several configurations. This integrative approach highlights the complex dynamics AIGC tool provides strategic insights their effective use education. As findings based on context, further exploration other educational settings encouraged to validate broader applicability.
Language: Английский
Citations
1
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 110406 - 110417
Published: Jan. 1, 2024
The advancement of Intelligent vehicle is leading to a growing prevalence and importance automotive interactive interfaces, attracting considerable research focus. With the increasing trend global aging, number elderly drivers on rise. Research search performance automobile interface for users has yet be carried out. Based this, this adopts eye tracking technology explore effects varying icon colors preferences perceptions within car-human employing eye-tracking gauge their impact. This work aims improve driving experience by enhancing drivers' information processing capabilities interaction comfort with in-car interfaces. In study, we examined six distinct foreground against two background in designs, conducting experiments standard indoor lighting. analysis results show that have faster speeds orange icons slowest yellow icons. Additionally, variation times different more pronounced white background. These conclusions hold significant implications future designers. They can leverage these optimize designs thereby safety experience, contributing efforts transportation industry.
Language: Английский
Citations
1
IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 140144 - 140159
Published: Jan. 1, 2024
This study investigates age-related distinctions in preferences for self-driving vehicles, exploring their connections with traffic-related elements and individual perceptions. Analyzing two groups (23-44 60+ years old), the research uncovers nuanced findings that offer valuable insights designing driver preference-based autonomous driving. The elder (Old: 60+) group, despite displaying elevated trust levels, exhibits lower compared to young-to-middle (Y-M: 23-44) aged group. discrepancy is highlighted alongside a significant significance between perceived difficulty both age groups. At each traffic situation, group lacks statistical significances complexity difficulty, signaling more intricate perception process. Correspondence analysis underscores age-specific extreme human-engaged or -disengaged actions, handing over control no informing, emphasizing of situation-specific considerations. Correlations current preference choices align As result, we suggest various design considerations could potentially improve driver-preference factor; customizable gradual automation transitions, factor-specific scaling, specific cut-off threshold, etc. not only reveals variations but also provides potential principles decision-making systems vehicles (AVs).
Language: Английский
Citations
0
Published: Nov. 22, 2024
Language: Английский
Citations
0
Research Square (Research Square), Journal Year: 2023, Volume and Issue: unknown
Published: Oct. 6, 2023
Abstract New technologies can help older persons age in place and support their caregivers. However, they need to be accepted by the end-users do so. Technology acceptance models, such as TAM UTAUT extensions, use factors like performance expectancy effort explain acceptance. Furthermore, are based on quantitative methods. Our qualitative study investigates fostering hindering among caregivers for a variety of assistive technologies, including wearables, ambient sensors at home with without cameras social companion robots. The goal this paper is twofold: On one hand, it technology models setting. other informs these aspects currently overlooked them. results reveal that relevant We also find reliability, anxiety around different have an influence aged care all end-user groups. findings used update current provide in-depth knowledge about factors.
Language: Английский
Citations
1
BIO Web of Conferences, Journal Year: 2024, Volume and Issue: 86, P. 01107 - 01107
Published: Jan. 1, 2024
The average age of the participants in this research, which evaluated effects public display advertising smart cities, was found to be 31.2 years, with a gender distribution that is balanced. When compared prior review, exposure and memory rates showed 5% improvement recall 12% increase length, suggesting increased effectiveness reach. Purchase intent by 11.8% interaction levels improved 10%, according consumer engagement ratings. In addition, post-exposure attitudes demonstrated 2.7% relevance 5.4% rise likeability, highlighting favorable opinion advertising. These results contribute disciplines urban informatics providing insightful information on changing role setting cities.
Language: Английский
Citations
0
Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 3 - 14
Published: Jan. 1, 2024
Language: Английский
Citations
0
Published: Jan. 1, 2023
The development of intelligent vehicles, as a high-tech product, is strongly influenced by Sino-US relations, and the core component supply problems that have arisen from them. Therefore, China’s vehicle industry matter great concern worth studying. However, due to lack historical data on this topic, it difficult predict its trend using existing methods. Thus, paper constructs an analytical framework integrates scenario analysis, patent technology roadmap based reconfiguration thinking forecast industry. There are several important findings. First, different scenarios for in 2035 presented. Second, planning up 2050 developed, where key barriers affecting found. Those include current state artificial intelligence technology, overall investment required including need build road network, geopolitics, particularly those regarding chip supply. Finally, solutions proposed future vehicles with focus Internet + reconstruction thinking.
Language: Английский
Citations
0
2020 IEEE 5th International Conference on Intelligent Transportation Engineering (ICITE), Journal Year: 2023, Volume and Issue: unknown, P. 237 - 241
Published: Oct. 28, 2023
Language: Английский
Citations
0