The Translation of Inclusion/Acceptance, Accessibility, and Empathy with Online Community Engagement

Service-learning at Virginia Commonwealth University traditionally involves students performing community service to address community needs, coupled with guided reflection for holistic growth. In the spring of 2020 in-person courses were suspended due to the Covid-19 pandemic, necessitating a transition to online classes. This study aimed to determine if online service-learning provided the same benefits as in-person experiences, focusing on students\u27 perceptions of inclusion/acceptance, empathy, and accessibility. Online surveys were administered to students enrolled in service-learning courses during the semester. Results showed reduced levels of support compared to Fall 2019, but increased social activism and awareness. The study noted that remote learning may have affected accessibility and awareness of inequities. The author recommends that the university continues this line of research to better understand how the changes in higher education influence the practice of service-learning and related students and community members. Author\u27s note My name is Jessie Feng, a graduate of Virginia Commonwealth University in 2021, with a B.S. in Biology and minors in Chemistry and Psychology. Currently, I am working in the medical field, with aspirations of matriculating into medical school. My experiences as a Service-Learning Teacher’s Assistant inspired this project, as I first-hand witnessed the empowering impact of service-learning. With the onset of the pandemic and the subsequent transition to online courses, I found it imperative to explore whether these benefits could translate to online community engagement to ultimately assist in the strengthening of service-learning programs. This piece has undergone substantial revisions for various purposes, this product was initially developed for the VCU Undergraduate Research and Creative Scholarship Summer Fellowship followed by the VCU Poster Symposium. From there, this project was revised for the 2021 Gulf South Summit, and now the VA Engage Journal. The final product is almost unrecognizable from my original submissions and I could not be more proud of this work. I am excited to share the results of this rewarding experience with a broader audience. I could not have achieved this level of success without the support of my service-learning mentors at VCU: Dr. Amanda Hall, Prof Katie Elliott, and Prof Jill Reid. I am forever grateful for the opportunities and support they provided during my undergraduate career. Additionally, I would also like to thank my wonderful boyfriend, who constantly reminded me of why I took up these projects and never let me quit. Lastly, I would like to thank Dr. Steve Grande for his thorough feedback, meaningful discussions, and unwavering support throughout this publishing process. All opinions expressed in this paper are my own and I have no conflicts of interest to disclose. Correspondence concerning this article should be addressed to email: [email protected]

Fast and accurate simulations of transmission-line metamaterials using transmission-matrix method

Recently, two-dimensional (2D) periodically L and C loaded transmission-line (TL) networks have been applied to represent metamaterials. The commercial Agilent's Advanced Design System (ADS) is a commonly-used tool to simulate the TL metamaterials. However, it takes a lot of time to set up the TL network and perform numerical simulations using ADS, making the metamaterial analysis inefficient, especially for large-scale TL networks. In this paper, we propose transmission-matrix method (TMM) to simulate and analyze the TL-network metamaterials efficiently. Compared to the ADS commercial software, TMM provides nearly the same simulation results for the same networks. However, the model-process and simulation time has been greatly reduced. The proposed TMM can serve as an efficient tool to study the TL-network metamaterials.Comment: 15 pages, 13 figure

Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability

Technological advances in the automotive industry are bringing automated driving closer to road use. However, one of the most important factors affecting public acceptance of automated vehicles (AVs) is the public's trust in AVs. Many factors can influence people's trust, including perception of risks and benefits, feelings, and knowledge of AVs. This study aims to use these factors to predict people's dispositional and initial learned trust in AVs using a survey study conducted with 1175 participants. For each participant, 23 features were extracted from the survey questions to capture his or her knowledge, perception, experience, behavioral assessment, and feelings about AVs. These features were then used as input to train an eXtreme Gradient Boosting (XGBoost) model to predict trust in AVs. With the help of SHapley Additive exPlanations (SHAP), we were able to interpret the trust predictions of XGBoost to further improve the explainability of the XGBoost model. Compared to traditional regression models and black-box machine learning models, our findings show that this approach was powerful in providing a high level of explainability and predictability of trust in AVs, simultaneously

Psychophysiological responses to takeover requests in conditionally automated driving

In SAE Level 3 automated driving, taking over control from automation raises significant safety concerns because drivers out of the vehicle control loop have difficulty negotiating takeover transitions. Existing studies on takeover transitions have focused on drivers' behavioral responses to takeover requests (TORs). As a complement, this exploratory study aimed to examine drivers' psychophysiological responses to TORs as a result of varying non-driving-related tasks (NDRTs), traffic density and TOR lead time. A total number of 102 drivers were recruited and each of them experienced 8 takeover events in a high fidelity fixed-base driving simulator. Drivers' gaze behaviors, heart rate (HR) activities, galvanic skin responses (GSRs), and facial expressions were recorded and analyzed during two stages. First, during the automated driving stage, we found that drivers had lower heart rate variability, narrower horizontal gaze dispersion, and shorter eyes-on-road time when they had a high level of cognitive load relative to a low level of cognitive load. Second, during the takeover transition stage, 4s lead time led to inhibited blink numbers and larger maximum and mean GSR phasic activation compared to 7s lead time, whilst heavy traffic density resulted in increased HR acceleration patterns than light traffic density. Our results showed that psychophysiological measures can indicate specific internal states of drivers, including their workload, emotions, attention, and situation awareness in a continuous, non-invasive and real-time manner. The findings provide additional support for the value of using psychophysiological measures in automated driving and for future applications in driver monitoring systems and adaptive alert systems.University of Michigan McityPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162593/1/AAP_physiological_responses_HF_template.pdfSEL

Analyzing Customer Needs of Product Ecosystems Using Online Product Reviews

It is necessary to analyze customer needs of a product ecosystem in order to increase customer satisfaction and user experience, which will, in turn, enhance its business strategy and profits. However, it is often time-consuming and challenging to identify and analyze customer needs of product ecosystems using traditional methods due to numerous products and services as well as their interdependence within the product ecosystem. In this paper, we analyzed customer needs of a product ecosystem by capitalizing on online product reviews of multiple products and services of the Amazon product ecosystem with machine learning techniques. First, we filtered the noise involved in the reviews using a fastText method to categorize the reviews into informative and uninformative regarding customer needs. Second, we extracted various customer needs related topics using a latent Dirichlet allocation technique. Third, we conducted sentiment analysis using a valence aware dictionary and sentiment reasoner method, which not only predicted the sentiment of the reviews, but also its intensity. Based on the first three steps, we classified customer needs using an analytical Kano model dynamically. The case study of Amazon product ecosystem showed the potential of the proposed method.https://deepblue.lib.umich.edu/bitstream/2027.42/153962/1/ANALYZING CUSTOMER NEEDS OF PRODUCT ECOSYSTEMS USING ONLINE PRODUCT REVIEWS.pdfDescription of ANALYZING CUSTOMER NEEDS OF PRODUCT ECOSYSTEMS USING ONLINE PRODUCT REVIEWS.pdf : Main articl

Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving

In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving-related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers’ takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers’ takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not

Building Trust Profiles in Conditionally Automated Driving

Trust is crucial for ensuring the safety, security, and widespread adoption of automated vehicles (AVs), and if trust is lacking, drivers and the public may not be willing to use them. This research seeks to investigate trust profiles in order to create personalized experiences for drivers in AVs. This technique helps in better understanding drivers' dynamic trust from a persona's perspective. The study was conducted in a driving simulator where participants were requested to take over control from automated driving in three conditions that included a control condition, a false alarm condition, and a miss condition with eight takeover requests (TORs) in different scenarios. Drivers' dispositional trust, initial learned trust, dynamic trust, personality, and emotions were measured. We identified three trust profiles (i.e., believers, oscillators, and disbelievers) using a K-means clustering model. In order to validate this model, we built a multinomial logistic regression model based on SHAP explainer that selected the most important features to predict the trust profiles with an F1-score of 0.90 and accuracy of 0.89. We also discussed how different individual factors influenced trust profiles which helped us understand trust dynamics better from a persona's perspective. Our findings have important implications for designing a personalized in-vehicle trust monitoring and calibrating system to adjust drivers' trust levels in order to improve safety and experience in automated driving

Investigating HMIs to Foster Communications between Conventional Vehicles and Autonomous Vehicles in Intersections

In mixed traffic environments that involve conventional vehicles (CVs) and autonomous vehicles (AVs), it is crucial for CV drivers to maintain an appropriate level of situation awareness to ensure safe and efficient interactions with AVs. This study investigates how AV communication through human-machine interfaces (HMIs) affects CV drivers' situation awareness (SA) in mixed traffic environments, especially at intersections. Initially, we designed eight HMI concepts through a human-centered design process. The two highest-rated concepts were selected for implementation as external and internal HMIs (eHMIs and iHMIs). Subsequently, we designed a within-subjects experiment with three conditions, a control condition without any communication HMI, and two treatment conditions utilizing eHMIs and iHMIs as communication means. We investigated the effects of these conditions on 50 participants acting as CV drivers in a virtual environment (VR) driving simulator. Self-reported assessments and eye-tracking measures were employed to evaluate participants' situation awareness, trust, acceptance, and mental workload. Results indicated that the iHMI condition resulted in superior SA among participants and improved trust in AV compared to the control and eHMI conditions. Additionally, iHMI led to a comparatively lower increase in mental workload compared to the other two conditions. Our study contributes to the development of effective AV-CV communications and has the potential to inform the design of future AV systems