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}
Pub Date : 2023-10-25DOI: 10.1177/21695067231192889
Liam Kettle, Yi-Ching Lee
Numerous collisions have occurred since integrating vehicles with automated driving system (ADS) features. Attributed responsibility following collisions differ dependent on level of automation or anthropomorphism. However, gender differences are yet to be examined. Thus, the current study examined gender differences in responsibility assignment following collisions involving an ADS-equipped vehicle and the influence of a driving assistant (DA) that administered monitoring requests. Participants read hypothetical scenarios and watched corresponding simulated videos with or without the DA present before assigning blame to the human or the ADS. Hypotheses included gender differences in overall blame assignment, and interaction effects between gender and DA presence; gender and agent; and gender, DA presence, and agent. Results indicated gender differences when assigning responsibility to the human agent only. No other significant differences were supported indicating that men and women generally attribute blame similarly. However, further demographic differences (e.g., age, socio-economic status) should be explored.
{"title":"Gender Differences in Responsibility Assignment Towards Level 3-ADS Vehicles","authors":"Liam Kettle, Yi-Ching Lee","doi":"10.1177/21695067231192889","DOIUrl":"https://doi.org/10.1177/21695067231192889","url":null,"abstract":"Numerous collisions have occurred since integrating vehicles with automated driving system (ADS) features. Attributed responsibility following collisions differ dependent on level of automation or anthropomorphism. However, gender differences are yet to be examined. Thus, the current study examined gender differences in responsibility assignment following collisions involving an ADS-equipped vehicle and the influence of a driving assistant (DA) that administered monitoring requests. Participants read hypothetical scenarios and watched corresponding simulated videos with or without the DA present before assigning blame to the human or the ADS. Hypotheses included gender differences in overall blame assignment, and interaction effects between gender and DA presence; gender and agent; and gender, DA presence, and agent. Results indicated gender differences when assigning responsibility to the human agent only. No other significant differences were supported indicating that men and women generally attribute blame similarly. However, further demographic differences (e.g., age, socio-economic status) should be explored.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"48 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":"135170618","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: 10.1177/21695067231195914
Ahmad Raza Usmani, Susan E. Kotowski, Kermit G. Davis
Many patient falls in healthcare facilities can be attributed to bed ingress and egress tasks. While bed height has been used as a means of fall injury prevention, some positions may actually place the patient at a biomechanical disadvantage during ingress and egress tasks, increasing fall risk, but this interaction is not well understood. Therefore, this study investigated the interaction between bed height, gender, and biomechanical outcomes of ground reaction forces to determine fall risk changes as a function of bed height. Participants were 24 healthy individuals who completed 72 trials. Results showed the main effects of bed height and gender were significant (p<0.05), but not the interaction for both vertical and anterior-posterior forces. Males had significantly greater forces in both directions, and the force in both directions were lowest under approximately a 66 cm bed height.
{"title":"The Impact of Hospital Bed Height and Gender on Fall Risk During Bed Egress","authors":"Ahmad Raza Usmani, Susan E. Kotowski, Kermit G. Davis","doi":"10.1177/21695067231195914","DOIUrl":"https://doi.org/10.1177/21695067231195914","url":null,"abstract":"Many patient falls in healthcare facilities can be attributed to bed ingress and egress tasks. While bed height has been used as a means of fall injury prevention, some positions may actually place the patient at a biomechanical disadvantage during ingress and egress tasks, increasing fall risk, but this interaction is not well understood. Therefore, this study investigated the interaction between bed height, gender, and biomechanical outcomes of ground reaction forces to determine fall risk changes as a function of bed height. Participants were 24 healthy individuals who completed 72 trials. Results showed the main effects of bed height and gender were significant (p<0.05), but not the interaction for both vertical and anterior-posterior forces. Males had significantly greater forces in both directions, and the force in both directions were lowest under approximately a 66 cm bed height.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"31 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":"135113294","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: 10.1177/21695067231192631
Wafic Chahine, Nour Hachem, Nadine Marie Moacdieh
Adaptive displays have long been touted as a means of improving the usability of different types of interfaces. However, purely eye tracking-based adaptive displays have not yet lived up to the initial promise. In many cases, adaptive displays are tailored to users with special needs, developed to supplement virtual reality, or combine eye tracking with other physiological measures. This mapping review focuses instead on recent adaptive displays that rely solely on eye tracking input to understand a user’s needs while interacting with a regular computer display. We aimed to answer three main research questions related to 1) the application domains of such adaptive displays, 2) the eye tracking metrics that have been adopted to track attention allocation in real time, and 3) the adaptation triggering mechanisms. We provide a summary of the current state of eye tracking-based adaptive displays, identify gaps in the literature, and suggest topics for future work.
{"title":"Eye Tracking-Based Adaptive Displays: A Review of the Recent Literature","authors":"Wafic Chahine, Nour Hachem, Nadine Marie Moacdieh","doi":"10.1177/21695067231192631","DOIUrl":"https://doi.org/10.1177/21695067231192631","url":null,"abstract":"Adaptive displays have long been touted as a means of improving the usability of different types of interfaces. However, purely eye tracking-based adaptive displays have not yet lived up to the initial promise. In many cases, adaptive displays are tailored to users with special needs, developed to supplement virtual reality, or combine eye tracking with other physiological measures. This mapping review focuses instead on recent adaptive displays that rely solely on eye tracking input to understand a user’s needs while interacting with a regular computer display. We aimed to answer three main research questions related to 1) the application domains of such adaptive displays, 2) the eye tracking metrics that have been adopted to track attention allocation in real time, and 3) the adaptation triggering mechanisms. We provide a summary of the current state of eye tracking-based adaptive displays, identify gaps in the literature, and suggest topics for future work.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"12 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":"135113639","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}
There are various studies to investigate the factors that affect driving experiences in the automobile environment and auditory experiences as variables. These factors might eventually affect users' sensitivity and performance of driving-related and non-driving-related tasks. Research on mechanical and electrical technology development is significant and has been conducted a lot. For driver- or passenger-centered auditory experiences, however, it is very important to understand people's perceptions and evaluations according to the technical design of the car sound. Therefore, in this paper, we would like to identify trends in 10 years of research on in-vehicle auditory experiences studied since 2013 and propose a human-centered automotive sound evaluation framework. The driving context, sound, and people were derived as factors affecting the car hearing experience, and specific sub-factors were derived. The results of this study are expected to provide insight into directionality in future automotive auditory.
{"title":"A Systematic Literature Review for Measure, Estimation, and Mitigation of Motion Sickness in Vehicle Environment","authors":"Yein Song, Myung Bin Choi, Sung hee Ahn, Myung Hwan Yun","doi":"10.1177/21695067231192257","DOIUrl":"https://doi.org/10.1177/21695067231192257","url":null,"abstract":"There are various studies to investigate the factors that affect driving experiences in the automobile environment and auditory experiences as variables. These factors might eventually affect users' sensitivity and performance of driving-related and non-driving-related tasks. Research on mechanical and electrical technology development is significant and has been conducted a lot. For driver- or passenger-centered auditory experiences, however, it is very important to understand people's perceptions and evaluations according to the technical design of the car sound. Therefore, in this paper, we would like to identify trends in 10 years of research on in-vehicle auditory experiences studied since 2013 and propose a human-centered automotive sound evaluation framework. The driving context, sound, and people were derived as factors affecting the car hearing experience, and specific sub-factors were derived. The results of this study are expected to provide insight into directionality in future automotive auditory.","PeriodicalId":74544,"journal":{"name":"Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Human Factors and Ergonomics Society. Annual meeting","volume":"38 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":"135113697","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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