Pub Date : 2023-10-25DOI: 10.1177/21695067231192609
Lilit Sargsyan, Seungju Choi, Sang-Hwan Kim
As the use of voice assistant (VA) systems is increasing, conversation design in the system is important for effective human-system interaction. The objective of the study was to investigate the level of user preference for VA outputs in terms of linguistics. Answers of three VA systems for each of the nine questions were collected and categorized for distinctive linguistic factors such as type of theme, thematic progression, number of predications, and ellipsis. The VA answers were evaluated through an online survey. Results show that linguistic factors and features significantly affect user preference for VA outputs. The results imply that the linguistic features need to be considered for designing voice interaction communications as a natural interaction method.
{"title":"Investigating User Preferences for Conversation Design of Voice Assistant Systems using Linguistic Features","authors":"Lilit Sargsyan, Seungju Choi, Sang-Hwan Kim","doi":"10.1177/21695067231192609","DOIUrl":"https://doi.org/10.1177/21695067231192609","url":null,"abstract":"As the use of voice assistant (VA) systems is increasing, conversation design in the system is important for effective human-system interaction. The objective of the study was to investigate the level of user preference for VA outputs in terms of linguistics. Answers of three VA systems for each of the nine questions were collected and categorized for distinctive linguistic factors such as type of theme, thematic progression, number of predications, and ellipsis. The VA answers were evaluated through an online survey. Results show that linguistic factors and features significantly affect user preference for VA outputs. The results imply that the linguistic features need to be considered for designing voice interaction communications as a natural interaction method.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192538
Liwei Qing, Bingyi Su, Ziyang Xie, Sehee Jung, Lu Lu, Hanwen Wang, Xu Xu, Edward P. Fitts
In recent years, there has been a trend to adopt human-robot collaboration (HRC) in the industry. In previous studies, computer vision-aided human pose reconstruction is applied to find the optimal position of point of operation in HRC that can reduce workers’ musculoskeletal disorder (MSD) risks due to awkward working postures. However, the reconstruction of human pose through computer-vision may fail due to the complexity of the workplace environment. In this study, we propose a data-driven method for optimizing the position of point of operation during HRC. A conditional variational auto-encoder (cVAE) model-based approach is adopted, which includes three steps. First, a cVAE model was trained using an open-access multimodal human posture dataset. After training, this model can output a simulated worker posture of which the hand position can reach a given position of point of operation. Next, an awkward posture score is calculated to evaluate MSD risks associated with the generated postures with a variety of positions of point of operation. The position of point of operation that is associated with a minimum awkward posture score is then selected for an HRC task. An experiment was conducted to validate the effectiveness of this method. According to the findings, the proposed method produced a point of operation position that was similar to the one chosen by participants through subjective selection, with an average difference of 4.5 cm.
{"title":"A Conditional Variational Auto-encoder Model for Reducing Musculoskeletal Disorder Risk during a Human-Robot Collaboration Task","authors":"Liwei Qing, Bingyi Su, Ziyang Xie, Sehee Jung, Lu Lu, Hanwen Wang, Xu Xu, Edward P. Fitts","doi":"10.1177/21695067231192538","DOIUrl":"https://doi.org/10.1177/21695067231192538","url":null,"abstract":"In recent years, there has been a trend to adopt human-robot collaboration (HRC) in the industry. In previous studies, computer vision-aided human pose reconstruction is applied to find the optimal position of point of operation in HRC that can reduce workers’ musculoskeletal disorder (MSD) risks due to awkward working postures. However, the reconstruction of human pose through computer-vision may fail due to the complexity of the workplace environment. In this study, we propose a data-driven method for optimizing the position of point of operation during HRC. A conditional variational auto-encoder (cVAE) model-based approach is adopted, which includes three steps. First, a cVAE model was trained using an open-access multimodal human posture dataset. After training, this model can output a simulated worker posture of which the hand position can reach a given position of point of operation. Next, an awkward posture score is calculated to evaluate MSD risks associated with the generated postures with a variety of positions of point of operation. The position of point of operation that is associated with a minimum awkward posture score is then selected for an HRC task. An experiment was conducted to validate the effectiveness of this method. According to the findings, the proposed method produced a point of operation position that was similar to the one chosen by participants through subjective selection, with an average difference of 4.5 cm.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"202 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192273
Braden Westby, Richard Stone, Colten Fales, Desmond Bonner
In a delicate balancing act between improving public relations and enhancing functionality and safety, law enforcement agencies often revisit the standards for their uniforms. Many experiments have been conducted over the years in reference to uniform color, but comparatively little research has been conducted relating to the implementation of accessories. In this study, we demonstrate that the use of “formal accessories” (as worn on a Class A uniform) may impact the public’s perception of police, particularly in reference to their perceived professionalism, authority, competence, and approachability.
{"title":"Law Enforcement Uniforms and Public Perception: An Overview and Pilot Study","authors":"Braden Westby, Richard Stone, Colten Fales, Desmond Bonner","doi":"10.1177/21695067231192273","DOIUrl":"https://doi.org/10.1177/21695067231192273","url":null,"abstract":"In a delicate balancing act between improving public relations and enhancing functionality and safety, law enforcement agencies often revisit the standards for their uniforms. Many experiments have been conducted over the years in reference to uniform color, but comparatively little research has been conducted relating to the implementation of accessories. In this study, we demonstrate that the use of “formal accessories” (as worn on a Class A uniform) may impact the public’s perception of police, particularly in reference to their perceived professionalism, authority, competence, and approachability.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"57 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192215
Elaheh Mehrabi, Tianhao Xu, Prashanth Rajivan
Despite extensive research on phishing, a severe lack of work centered on attackers has resulted in a limited understanding of the adversarial behaviors conducive to attack success and failures. This work describes a novel method for conducting controlled laboratory studies of cognitive vulnerabilities that attackers experience during the design and execution phases of spear-phishing attacks. Based on the SpearSim platform, the new simulation environment integrates cognitive agents that model and predict end-user responses to spear-phishing attacks. This advancement to SpearSim allows the generation of real-time, automated, “human-like” responses to simulated spear-phishing attacks. This enables the execution of experiments focused on attackers and attacker behaviors. We describe the proposed simulation framework, provide details about the implemented simulation environment, and present results to evaluate the performance of the simulation environment. Compared to the earlier version of SpearSim involving human end-users, the new approach generates responses at a much faster rate (3 times faster than human end-users) and importantly with less variance in the time to respond. The cognitive agents used in the simulation predicted human responses to phishing and spear-phishing attackers with moderate accuracy (about 60%). Our proposed method intends to provide an effective and robust way to conduct laboratory experiments on spear-phishing attacks and further understand attackers' decision-making processes that could be exploited to thwart future attacks.
{"title":"SpearSim-V<sub>2</sub>: Synthetic Task Environment for Evaluating Attacker Behaviors","authors":"Elaheh Mehrabi, Tianhao Xu, Prashanth Rajivan","doi":"10.1177/21695067231192215","DOIUrl":"https://doi.org/10.1177/21695067231192215","url":null,"abstract":"Despite extensive research on phishing, a severe lack of work centered on attackers has resulted in a limited understanding of the adversarial behaviors conducive to attack success and failures. This work describes a novel method for conducting controlled laboratory studies of cognitive vulnerabilities that attackers experience during the design and execution phases of spear-phishing attacks. Based on the SpearSim platform, the new simulation environment integrates cognitive agents that model and predict end-user responses to spear-phishing attacks. This advancement to SpearSim allows the generation of real-time, automated, “human-like” responses to simulated spear-phishing attacks. This enables the execution of experiments focused on attackers and attacker behaviors. We describe the proposed simulation framework, provide details about the implemented simulation environment, and present results to evaluate the performance of the simulation environment. Compared to the earlier version of SpearSim involving human end-users, the new approach generates responses at a much faster rate (3 times faster than human end-users) and importantly with less variance in the time to respond. The cognitive agents used in the simulation predicted human responses to phishing and spear-phishing attackers with moderate accuracy (about 60%). Our proposed method intends to provide an effective and robust way to conduct laboratory experiments on spear-phishing attacks and further understand attackers' decision-making processes that could be exploited to thwart future attacks.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"62 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192576
Kelvin Kwakye, Armstrong Aboah, Younho Seong, Sun Yi
Distracted driving is a dangerous driving behavior that causes numerous accidents on US roads each year. It is critical to identify distracted drivers in order to prevent such accidents. Previous studies attempted to detect distracted driving using heuristics and machine learning; however, none of these methods could capture the problem's spatiotemporal features. As a result, the purpose of this study was to use a 3D convolutional neural network (CNN) that can capture both spatial and temporal information to classify distracted drivers based on facial features and behavioral cues. We used the Database to Enable Facial Analysis for Driving Studies (DEFADS), an open-source dataset containing 77 human subjects performing scripted driving-related activities, to achieve this goal. The PyTorch video library was used to train the model. The 3D CNN achieved an overall recall and precision of 97.6 and 98.1, respectively, indicating its efficacy in detecting distracted drivers in the real world.
{"title":"Classification of Human Driver Distraction Using 3D Convolutional Neural Networks","authors":"Kelvin Kwakye, Armstrong Aboah, Younho Seong, Sun Yi","doi":"10.1177/21695067231192576","DOIUrl":"https://doi.org/10.1177/21695067231192576","url":null,"abstract":"Distracted driving is a dangerous driving behavior that causes numerous accidents on US roads each year. It is critical to identify distracted drivers in order to prevent such accidents. Previous studies attempted to detect distracted driving using heuristics and machine learning; however, none of these methods could capture the problem's spatiotemporal features. As a result, the purpose of this study was to use a 3D convolutional neural network (CNN) that can capture both spatial and temporal information to classify distracted drivers based on facial features and behavioral cues. We used the Database to Enable Facial Analysis for Driving Studies (DEFADS), an open-source dataset containing 77 human subjects performing scripted driving-related activities, to achieve this goal. The PyTorch video library was used to train the model. The 3D CNN achieved an overall recall and precision of 97.6 and 98.1, respectively, indicating its efficacy in detecting distracted drivers in the real world.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tragus expansion angle (TEA) is an angular variable that quantifies the degree of outward expansion of the tragus cartilage induced by in-ear wearables worn in the human ear. However, the TEA cannot be measured directly, and the mechanism that explains how expansion variations affect users’ comfort experience is not well understood. The purpose of this study was to establish a quantitative relationship between variations in the tragus expansion angle and users’ comfort experience. TEA was measured on 400 healthy participants and normalized using a measuring device (ATMC prototype) and Tragus Expansion Index (TEI). Our results show that the comfort range across variations in TEA was similar for both sexes, yet compared to females, males could tolerate larger variations both in TEA and TEI. A quantitative relationship was established using TEI values, (dis)comfort ratings and GaussAmp function, which can be employed for ergonomic design purposes.
耳屏扩张角(Tragus expansion angle, TEA)是一个角度变量，用于量化人耳中佩戴入耳式可穿戴设备引起耳屏软骨向外扩张的程度。然而，TEA不能直接测量，并且解释膨胀变化如何影响用户舒适体验的机制尚不清楚。本研究的目的是建立耳屏扩张角变化与使用者舒适体验之间的定量关系。对400名健康受试者进行TEA测量，并使用测量仪(ATMC原型)和Tragus Expansion Index (TEI)进行归一化。我们的研究结果表明，男女对TEA变化的舒适范围是相似的，但与女性相比，男性可以忍受更大的TEA和TEI变化。使用TEI值、(dis)舒适评级和GaussAmp函数建立定量关系，可用于人体工程学设计目的。
{"title":"Effects of Variations in the Tragus Expansion Angle on Users’ Comfort for In-ear Wearables","authors":"Hao Fan, Mengcheng Wang, Xiao Zhao, Yihui Ren, Chen Chen, Yunjie Dou, Jinlei Shi, Dengkai Chen, Carisa Harris-Adamson, Chunlei Chai","doi":"10.1177/21695067231192616","DOIUrl":"https://doi.org/10.1177/21695067231192616","url":null,"abstract":"Tragus expansion angle (TEA) is an angular variable that quantifies the degree of outward expansion of the tragus cartilage induced by in-ear wearables worn in the human ear. However, the TEA cannot be measured directly, and the mechanism that explains how expansion variations affect users’ comfort experience is not well understood. The purpose of this study was to establish a quantitative relationship between variations in the tragus expansion angle and users’ comfort experience. TEA was measured on 400 healthy participants and normalized using a measuring device (ATMC prototype) and Tragus Expansion Index (TEI). Our results show that the comfort range across variations in TEA was similar for both sexes, yet compared to females, males could tolerate larger variations both in TEA and TEI. A quantitative relationship was established using TEI values, (dis)comfort ratings and GaussAmp function, which can be employed for ergonomic design purposes.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"82 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192573
Lucas Layman, William Roden
Organizations use intrusion detection systems (IDSes) to identify harmful activity among millions of computer network events. Cybersecurity analysts review IDS alarms to verify whether malicious activity occurred and to take remedial action. However, IDS systems exhibit high false alarm rates. This study examines the impact of IDS false alarm rate on human analyst sensitivity (probability of detection), precision (positive predictive value), and time on task when evaluating IDS alarms. A controlled experiment was conducted with participants divided into two treatment groups, 50% IDS false alarm rate and 86% false alarm rate, who classified whether simulated IDS alarms were true or false alarms. Results show statistically significant differences in precision and time on task. The median values for the 86% false alarm rate group were 47% lower precision and 40% slower time on task than the 50% false alarm rate group. No significant difference in analyst sensitivity was observed.
{"title":"A Controlled Experiment on the Impact of Intrusion Detection False Alarm Rate on Analyst Performance","authors":"Lucas Layman, William Roden","doi":"10.1177/21695067231192573","DOIUrl":"https://doi.org/10.1177/21695067231192573","url":null,"abstract":"Organizations use intrusion detection systems (IDSes) to identify harmful activity among millions of computer network events. Cybersecurity analysts review IDS alarms to verify whether malicious activity occurred and to take remedial action. However, IDS systems exhibit high false alarm rates. This study examines the impact of IDS false alarm rate on human analyst sensitivity (probability of detection), precision (positive predictive value), and time on task when evaluating IDS alarms. A controlled experiment was conducted with participants divided into two treatment groups, 50% IDS false alarm rate and 86% false alarm rate, who classified whether simulated IDS alarms were true or false alarms. Results show statistically significant differences in precision and time on task. The median values for the 86% false alarm rate group were 47% lower precision and 40% slower time on task than the 50% false alarm rate group. No significant difference in analyst sensitivity was observed.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"432 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135112823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231198084
Jeff C. Stanley, Stephen L. Dorton
Engineering trustworthy artificial intelligence (AI) is important to adoption and appropriate use, but there are challenges to implementing trustworthy AI systems. It is difficult to translate trust studies from the laboratory to the field. It is also difficult to operationalize “trustworthy AI” frameworks and principles to inform the actual development of AI. We address these challenges with an approach based in reported incidents of trust loss “in the wild.” We systematically identified 30 cases of trust loss in the AI Incident Database to gain insight into how and why humans lose trust in AI in various contexts. These factors could be codified into the development cycle in various forms such as checklists and design patterns to manage trust in AI systems and avoid similar incidents in the future. Because it is based in real incidents, this approach offers recommendations that are concrete and actionable for teams addressing real use cases with AI systems.
{"title":"Exploring Trust With the AI Incident Database","authors":"Jeff C. Stanley, Stephen L. Dorton","doi":"10.1177/21695067231198084","DOIUrl":"https://doi.org/10.1177/21695067231198084","url":null,"abstract":"Engineering trustworthy artificial intelligence (AI) is important to adoption and appropriate use, but there are challenges to implementing trustworthy AI systems. It is difficult to translate trust studies from the laboratory to the field. It is also difficult to operationalize “trustworthy AI” frameworks and principles to inform the actual development of AI. We address these challenges with an approach based in reported incidents of trust loss “in the wild.” We systematically identified 30 cases of trust loss in the AI Incident Database to gain insight into how and why humans lose trust in AI in various contexts. These factors could be codified into the development cycle in various forms such as checklists and design patterns to manage trust in AI systems and avoid similar incidents in the future. Because it is based in real incidents, this approach offers recommendations that are concrete and actionable for teams addressing real use cases with AI systems.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"13 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135113270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192894
Scott Mishler, Jing Chen
The boom of automated driving systems (ADS) promises to change the way humans drive and interact with their vehicle, especially when these systems receive new updates that may change the way they work. Human-automation teams need to ensure proper roles are established for who is in control of the driving task at any given time. The human needs to have properly calibrated trust to know how to properly work with the system during driving. Framing research shows that positive and negative framing can influence how individuals perceive and make decisions, and swift trust shows that trust can be created quickly in newly established teams. We draw from both realms of literature and tested how new updates of the ADS are framed to the driver with the goal of either promoting or dampening trust to ensure the human driver is maintaining proper trust calibration.
{"title":"Framing Updates: How Framing Influences Trust for Automated Driving Systems","authors":"Scott Mishler, Jing Chen","doi":"10.1177/21695067231192894","DOIUrl":"https://doi.org/10.1177/21695067231192894","url":null,"abstract":"The boom of automated driving systems (ADS) promises to change the way humans drive and interact with their vehicle, especially when these systems receive new updates that may change the way they work. Human-automation teams need to ensure proper roles are established for who is in control of the driving task at any given time. The human needs to have properly calibrated trust to know how to properly work with the system during driving. Framing research shows that positive and negative framing can influence how individuals perceive and make decisions, and swift trust shows that trust can be created quickly in newly established teams. We draw from both realms of literature and tested how new updates of the ADS are framed to the driver with the goal of either promoting or dampening trust to ensure the human driver is maintaining proper trust calibration.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"50 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135217085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-25DOI: 10.1177/21695067231194993
Nade Liang, Chiho Lim, Denny Yu, Kwaku O. Prakah-Asante, Brandon J. Pitts
Conditionally automated vehicles require drivers to take over control occasionally. To date, takeover performance has been mostly evaluated using only re-engagement time and quality metrics. However, the appropriateness of takeover decisions, which has not been considered by previous research, should also be included as a performance indicator as it reflects one’s situation awareness of the takeover scenario. The goal of this study was to use eye-tracking, demographic factors, workload, and non-driving-related task (NDRT) conditions to predict takeover decisions. Forty-three participants drove a simulated conditionally automated vehicle while performing visual NDRTs and needed to decide the most appropriate maneuver around a roadway obstacle. Six classifiers were used to predict takeover decisions. The Random Forest model achieved the best performance, and driving experience and perceived workload were the most influential features. Findings may be used to assist in the design of adaptive algorithms that support drivers taking over from automated vehicles.
{"title":"Predicting Automated Vehicle Takeover Decision During the Nighttime","authors":"Nade Liang, Chiho Lim, Denny Yu, Kwaku O. Prakah-Asante, Brandon J. Pitts","doi":"10.1177/21695067231194993","DOIUrl":"https://doi.org/10.1177/21695067231194993","url":null,"abstract":"Conditionally automated vehicles require drivers to take over control occasionally. To date, takeover performance has been mostly evaluated using only re-engagement time and quality metrics. However, the appropriateness of takeover decisions, which has not been considered by previous research, should also be included as a performance indicator as it reflects one’s situation awareness of the takeover scenario. The goal of this study was to use eye-tracking, demographic factors, workload, and non-driving-related task (NDRT) conditions to predict takeover decisions. Forty-three participants drove a simulated conditionally automated vehicle while performing visual NDRTs and needed to decide the most appropriate maneuver around a roadway obstacle. Six classifiers were used to predict takeover decisions. The Random Forest model achieved the best performance, and driving experience and perceived workload were the most influential features. Findings may be used to assist in the design of adaptive algorithms that support drivers taking over from automated vehicles.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"73 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135219064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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