Analyzing Factors Influencing Driver Willingness to Accept Advanced Driver Assistance Systems Hannah Musau a,, Nana Kankam Gyimah a, Judith Mwakalonge a, Gurcan Comert b, Saidi Siuhi a a Department of Engineering, South Carolina State University, Orangeburg, South Carolina, USA, 29117 b Department of Computational Engineering and Data Science, North Carolina A&T State University, Greensboro, North Carolina, US, 27411Abstract Advanced Driver Assistance Systems (ADAS) enhance highway safety by improving environmental perception and reducing human errors. However, misconceptions, trust issues, and knowledge gaps hinder widespread adoption. This study examines driver perceptions, knowledge sources, and usage patterns of ADAS in passenger vehicles. A nationwide survey collected data from a diverse sample of U.S. drivers. Machine learning models predicted ADAS adoption, with SHAP (SHapley Additive Explanations) identifying key influencing factors. Findings indicate that higher trust levels correlate with increased ADAS usage, while concerns about reliability remain a barrier. Findings emphasize the influence of socioeconomic, demographic, and behavioral factors on ADAS adoption, offering guidance for automakers, policymakers, and safety advocates to improve awareness, trust, and usability. Introduction Human factors are the leading cause of road crashes, contributing to over 90% of incidents either alone or alongside failures in vehicles or infrastructure [1].

This research explores the factors driving user acceptance of Rabbit R1, a newly developed portable intelligent personal assistant (PIPA) that aims to redefine user interaction and control. The study extends the technology acceptance model (TAM) by incorporating artificial intelligence-specific factors (conversational intelligence, task intelligence, and perceived naturalness), user interface design factors (simplicity in information design and visual aesthetics), and user acceptance and loyalty. Using a purposive sampling method, we gathered data from 824 users in the US and analyzed the sample through partial least squares structural equation modeling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). The findings reveal that all hypothesized relationships, including both direct and indirect effects, are supported. Additionally, fsQCA supports the PLS-SEM findings and identifies three configurations leading to high and low user acceptance. This research enriches the literature and provides valuable insights for system designers and marketers of PIPAs, guiding strategic decisions to foster widespread adoption and long-term engagement.

Physical activity is important for health and wellbeing, but only few fulfill the World Health Organization's criteria for physical activity. The development of a robotic exercise trainer can assist in increasing training accessibility and motivation. The acceptance and trust of users are crucial for the successful implementation of such an assistive robot. This can be affected by the transparency of the robotic system and the robot's performance, specifically, its failures. The study presents an initial investigation into the transparency levels as related to the task, human, robot, and interaction (T-HRI), with robot behavior adjusted accordingly. A failure in robot performance during part of the experiments allowed to analyze the effect of the T-HRI levels as related to failures. Participants who experienced failure in the robot's performance demonstrated a lower level of acceptance and trust than those who did not experience this failure. In addition, there were differences in acceptance measures between T-HRI levels and participant groups, suggesting several directions for future research.

Will patients ever trust AI in healthcare? As we witness the early transitioning to the'automated era'-- whereby AI and machine learning technologies alleviate the burden of manual and menial work-- it has become clear that its reception among its intended userbase will face a challenge of human resistance. Away from heavy industries-- factory floors, manufacturing, and supply-chains-- AI is increasingly handling interactions between business and customer. It is, perhaps, the most challenging test of the technology's ability to mimic human intelligence. Whether it's seeking reassurance in customer support, sound financial advice, or medical diagnoses, humans ability to make significant decisions with conviction innately requires a sense of trust.

While AI does deliver ever greater opportunities for efficiencies, people who hear about this immediately fear for their job. However, successful manufacturing companies know that the key is striking the right balance between robots and people. The first step is understanding what user needs robots address. The foundation of AI is pattern recognition. Once AI learns a pattern, it can use that pattern to make predictions about the outcomes of similar patterns.

Ubiquitous information access becomes more and more important nowadays and research is aimed at making it adapted to users. Our work consists in applying machine learning techniques in order to adapt the information access provided by ubiquitous systems to users when the system only knows the user social group, without knowing anything about the user interest. The adaptation procedures associate actions to perceived situations of the user. Associations are based on feedback given by the user as a reaction to the behavior of the system. Our method brings a solution to some of the problems concerning the acceptance of the system by users when applying machine learning techniques to systems at the beginning of the interaction between the system and the user.