Modelling Level 1 Situation Awareness in Driving: A Cognitive Architecture Approach

The goal of this research is to computationally model and simulate the collective drivers’ Level I Situation Awareness (SA). I developed a computational model in a cognitive architecture that can interact with a driving simulator to infer quantitative predictions of drivers’ SA. I demonstrate theoretical application of modelling and predicting SA from the lens of human cognition utilizing the Queueing Network-Adaptive Control of Thought Rational (QN-ACT-R) framework as a foundation. I integrated a dynamic visual sampling model (SEEV) with QN-ACT-R to create QN-ACT-R-SA which simulates realistic attention allocation patterns of human drivers at SA Level 1 (i.e. perception of critical elements). QN-ACT-R-SA also incorporates a driver model that can simulate human driving behaviors by interacting with a driving simulator. Three validation studies (Study I, II and III) were conducted to determine whether Level 1 SA results produced with the QN-ACT-R-SA model correspond to empirical data collected from human drivers for the same tasks. In Study I, QN-ACT-R-SA model was validated against probe-based SA measures and in Study II, the model was validated against a hazard perception-based SA measure. In Study III, model’s predictive power was assessed by comparing model results to a previously conducted empirical experiment. In Study I, two types of probe-based SA measures were used: within-task queries using Situation Awareness Global Assessment Technique (SAGAT), and post-experiment questions. A comparative assessment demonstrated that QN-ACT-R-SA could reasonably simulate drivers’ Level 1 SA for two driving conditions: easy (with few vehicles and signboards) and complex (with dense traffic and signboards). QN-ACT-R-SA fit for human SAGAT scores resulted in mean absolute percentage error (MAPE) of 5.02%, and the root mean square error (RMSE) of 3.47. Model fit for post-experiment human SA results were MAPE of 6.73%, and RMSE of 6.13. The RMSE of 3.47 for SAGAT responses indicate a small error difference between the average human and modelling results since the average SAGAT scores (measured on a scale of 0-100) for the easy and complex driving condition is around 71.9 (SD: 21.1). Similarly, the RMSE of 6.13 for post-experiment SA questionnaire also indicates a small error difference since the average post-experiment SA questionnaire score (on a scale of 0-100) for the easy and complex driving condition is around 73.8 (SD: 16.2). In Study II, Brake Perception Response Time (BPRT) was used as a hazard perception test to further assess the model’s ability to simulate drivers’ SA at Level 1. An empirical study was designed mainly for model validation purposes. In the trials runs, the participants encountered two major types of hazards: on-road hazards in the forward view of the driver and roadside hazard which originated from the driver’s periphery. The two contrasting conditions were selected to explore the difference in driver’s BPRT. The results demonstrated that BPRT was significantly shorter for on-road hazards as compared to roadside hazards. The overall model fitness for empirical BPRT results indicated an MAPE of 9.4 % and the RMSE of 0.13 seconds. The RMSE value in Study II indicates a small error difference between the average human and modelling results since the average BPRT for the two on-road and roadside hazard conditions is around 1.49 seconds (SD: 0.54). Study III involved extending the same modelling approach towards assessing the predictive power of QN-ACT-R-SA. The empirical data was taken from a previously conducted research study that had examined the effects of Adaptive Cruise Control (ACC) and cellphone use on drivers’ SA using SAGAT tests. QN-ACT-R-SA fit for predicting the effects of ACC and cellphone use on drivers’ Level 1 SA resulted in a MAPE of 5.6%, and the RMSE of 4.9. The RMSE of 4.9 for SAGAT responses indicates a small error difference between the average human and modelling results since the average SAGAT scores for the different driving conditions in Study III is around 72 (SD: 4.76). Both absolute (MAPE) and relative (RMSE) measures of goodness-of-fit confirm models efficacy in reasonably simulating human SA across the three studies. The MAPE value of less than 10% across the three studies show that the model’s deviation from the empirical results in terms of percentage error is relatively small. The graphical analysis of the average model versus average human plots further indicate that the model was able to successfully map the changes in SA scores across the different experimental conditions tested in the three studies. In summary, this research presents: 1) a model of collective drivers’ Level 1 SA that is grounded in cognitive and perceptual mechanisms of human information processing; 2) a real-time programmable implementation of the model as a simulation software; 3) validation of the model using empirical results drawn from established SA measures; and 4) new ideas towards modelling Level 2/3 SA and improving the existing modelling paradigm

Social Robots in Retail: Emotional Experiences a Critical Driver of Purchase Intention

The purpose of the current study is to explore whether emotional experiences prompted due to human-social robot interaction in retail environments significantly influence consumers' purchase intentions. This present study focuses primarily on emotional experience, comprising factors, namely, enjoyment, arousal, and emotional involvement. The study tests the conceptual model on a sample of 229 respondents using the PLS-SEM (Partial Least Squares – Structural Equation Modeling) approach. The results reveal that emotional experiences significantly impact consumers’ purchase intentions in retail settings. All three emotional experiences, including enjoyment, emotional involvement, and arousal were significant in shaping consumers' purchase intentions. The study findings offer unique insights for manufacturers developing social robots for the retail sector. The present research extends the current body of work exploring hedonic predictors of consumers' purchase intentions in novel socio-technical contexts, such as social robotics

A comparative assessment of human factors in cybersecurity: Implications for cyber governance

This paper provides an extensive overview of cybersecurity awareness in the young, educated, and technology-savvy population of the United Arab Emirates (UAE), compared to the United States of America (USA) for advancing the scholarship and practice of global cyber governance. We conducted comparative empirical studies to identify differences in specific human factors that affect cybersecurity behaviour in the UAE and the USA. In addition, we employed several control variables to observe reliable results. We used Hofstede’s theoretical framework on culture to advance our investigation. The results show that the targeted population in the UAE exhibits contrasting interpretations of cybersecurity awareness of critical human factors as compared to their counterparts from the USA. We identify possible explanations for this relatively different behaviour in the UAE population. Our key contributions are to provide valuable information for cybersecurity policymakers in the UAE and Gulf Cooperation Council (GCC) region to further enhance cyber safety, governance, awareness, and trust among citizens

An Alternate Account on the Ethical Implications of Autonomous Vehicles

Given the widespread popularity of Autonomous Vehicles (AVs), researchers have been exploring the ethical implications of AVs. Researchers believe that empirical experiments can provide insights into human characterization of ethically sound machine behavior. Previous research indicates that humans generally endorse utilitarian AVs, however, this paper explores an alternative account on the discourse of ethical decision-making in AVs. We refrain from favoring consequentialism or non-consequential ethical theories, and argue that human moral decision-making is pragmatic, or in other words, ethically and rationally bounded. We hold the perspective that our moral preferences shift based on various externalities and biases. To further this concept, we conduct two Amazon Mechanical Turk studies to investigate factors, such as, the \u27degree of harm\u27, and \u27level of affection\u27, which influence people\u27s moral decision-making. Our experimental findings seem to suggest that human moral judgements cannot be wholly deontological or utilitarian. We discovered that as the degree of harm decreased, people became less utilitarian (more deontological), and as the level of affection increased, people became less utilitarian (more deontological). These findings offer evidence on the ethical variations in human decision-making processes and refutes the view that aim to advocate application of a specific moral framework based on empirical evidence. The findings also offer useful insights for policymakers to explore the overall public perception on the ethical implications of AV

Access Permissions for Apple Watch Applications: A Study on Users\u27 Perceptions

© 2020 IEEE. The pervasiveness and sheer ubiquity of wearables, such as smartwatches, has given rise to a myriad of privacy concerns. In this paper, we examine the privacy issues which arise from the permission requests framework on Apple wearables and explore how end user\u27s perception of these can inform better and more inclusive privacy. We conduct an empirical study which explores issues pertaining to data protection, safety, trust, ethics, and cybersecurity. We conducted two Amazon Mechanical Turk studies that investigate users\u27 perception on app permission requests for different smartwatch applications. Our findings suggest that most users lack proper understanding of the cybersecurity risks posed and were unable to construe the rationale for permissions requests for popular smartwatch applications. Furthermore, the respondents believed that app developers might misuse their data, thereby, indicating lack of trust towards these app development enterprises. The respondents also believe that the application development companies should be held accountable for their alleged involvement in data breaches and privacy issues. Further, the majority of survey respondents indicated having some unease towards data usage policies of developers. Moreover, respondents consider all common types of private data (location, health and fitness, photos etc.) susceptible to some level of data breach. Lastly, our results indicate that the study participants experienced confusion in the \u27usability\u27 versus \u27security\u27 conundrum-while a bare majority of the users wanted ease of access, a similar minority preferred a higher level of security. We conclude by presenting a discussion to the quandaries that can help us interweave towards reliable, secure, trustworthy, and ethical technologies

Comparative Evaluation of Augmented Reality-based Assistance for Procedural Tasks: A Simulated Control Room Study

This is an Accepted Manuscript of an article published by Taylor & Francis in Behaviour & Information Technology in 2019, available online: https://doi.org/10.1080/0144929X.2019.1660805This research explores the design, implementation, and evaluation of a prototype augmented reality application that assists operators in performing procedural tasks in control room settings. Our prototype uses a tablet display to supplement an operator’s natural view of existing control panel elements with sequences of interactive visual and attention guiding cues. An experiment, conducted using a nuclear power plant simulator, examined university students completing both standard and emergency operating procedures. The augmented reality condition was compared against two other conditions – a paper-based procedure condition using paper manuals and a computer-based procedure condition using digital procedures presented on a desktop display. The results demonstrated that the augmented reality -based procedure system had benefits in terms of reduced mental workload in comparison to the other two conditions. Regarding task completion time, accuracy, and situation awareness, the augmented reality condition had no significant difference when compared against the computer-based procedure condition but performed better than the paper-based procedure condition. It was also found that the augmented reality condition resulted in fewer intra-team inquiry communication exchanges in comparison to both paper-based and computer-based conditions. The augmented reality condition, however, yielded poorer memory retention score when assessed against the other two conditions

Underlying Effect of Customer Satisfaction on Repurchase Intentions: Mediating role of Trust and Commitment

Pakistani banking industry rivalry becomes more intense with the development of this sector.  This study aims to examine the relationship between customers satisfaction and repurchase intentions through the prologue of trust and commitment as mediators in the banking industry of Pakistan. 225 customers of banks was included in the analysis with 87% response rate. Results showed positive relationship between satisfaction and repurchase intentions, trust and repurchase intentions, trust and commitment. Satisfaction positively enhances the trust subsequently trust fosters the commitment and this increase the repurchase intentions of customer. Commitment and trust both are act as mediators between customers satisfaction and repurchase intentions. These findings have strategic implications for increasing customers repurchase intentions in banking industry of Pakistan. Keywords: Satisfaction, Trust, Commitment, Repurchase intention

Augmented Reality-based Indoor Navigation: A Comparative Analysis of Handheld Devices vs. Google Glass

© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. U. Rehman, & S. Cao. (2017). IEEE Transactions on Human-Machine Systems, 47(1), 140–151. https://doi.org/10.1109/THMS.2016.2620106Navigation systems have been widely used in outdoor environments, but indoor navigation systems are still in early development stages. In this paper, we introduced an augmented-reality-based indoor navigation application to assist people navigate in indoor environments. The application can be implemented on electronic devices such as a smartphone or a head-mounted device. In particular, we examined Google Glass as a wearable head-mounted device in comparison with handheld navigation aids including a smartphone and a paper map. We conducted both a technical assessment study and a human factors study. The technical assessment established the feasibility and reliability of the system. The human factors study evaluated human-machine system performance measures including perceived accuracy, navigation time, subjective comfort, subjective workload, and route memory retention. The results showed that the wearable device was perceived to be more accurate, but other performance and workload results indicated that the wearable device was not significantly different from the handheld smartphone. We also found that both digital navigation aids were better than the paper map in terms of shorter navigation time and lower workload, but digital navigation aids resulted in worse route retention. These results could provide empirical evidence supporting future designs of indoor navigation systems. Implications and future research were also discussed.This work was supported in part by NSERC Discovery Grant RGPIN-2015-04134