Pub Date : 2024-09-19DOI: 10.1109/THMS.2024.3458751
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2024.3458751","DOIUrl":"https://doi.org/10.1109/THMS.2024.3458751","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 5","pages":"C2-C2"},"PeriodicalIF":3.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1109/THMS.2024.3458753
{"title":"IEEE Systems, Man, and Cybernetics Society Information","authors":"","doi":"10.1109/THMS.2024.3458753","DOIUrl":"https://doi.org/10.1109/THMS.2024.3458753","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 5","pages":"C3-C3"},"PeriodicalIF":3.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684376","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1109/THMS.2024.3458769
{"title":"TechRxiv: Share Your Preprint Research with the World!","authors":"","doi":"10.1109/THMS.2024.3458769","DOIUrl":"https://doi.org/10.1109/THMS.2024.3458769","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 5","pages":"630-630"},"PeriodicalIF":3.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-19DOI: 10.1109/THMS.2024.3458755
{"title":"IEEE Transactions on Human-Machine Systems Information for Authors","authors":"","doi":"10.1109/THMS.2024.3458755","DOIUrl":"https://doi.org/10.1109/THMS.2024.3458755","url":null,"abstract":"","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 5","pages":"C4-C4"},"PeriodicalIF":3.5,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142246520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1109/THMS.2024.3407875
Hongguang Pan;Zhuoyi Li;Yunpeng Fu;Xuebin Qin;Jianchen Hu
Reconstructing visual stimulus representation is a significant task in neural decoding. Until now, most studies have considered functional magnetic resonance imaging (fMRI) as the signal source. However, fMRI-based image reconstruction methods are challenging to apply widely due to the complexity and high cost of acquisition equipment. Taking into account the advantages of the low cost and easy portability of electroencephalogram (EEG) acquisition equipment, we propose a novel image reconstruction method based on EEG signals in this article. First, to meet the high recognizability of visual stimulus images in a fast-switching manner, we construct a visual stimuli image dataset and obtain the corresponding EEG dataset through EEG signals collection experiment. Second, we introduce the deep visual representation model (DVRM), comprising a primary encoder and a subordinate decoder, to reconstruct visual stimuli representation. The encoder is designed based on residual-in-residual dense blocks to learn the distribution characteristics between EEG signals and visual stimulus images. Meanwhile, the decoder is designed using a deep neural network to reconstruct the visual stimulus representation from the learned deep visual representation. The DVRM can accommodate the deep and multiview visual features of the human natural state, resulting in more precise reconstructed images. Finally, we evaluate the DVRM based on the quality of the generated images using our EEG dataset. The results demonstrate that the DVRM exhibits an excellent performance in learning deep visual representation from EEG signals, generating reconstructed representation of images that are realistic and highly resemble the original images.
{"title":"Reconstructing Visual Stimulus Representation From EEG Signals Based on Deep Visual Representation Model","authors":"Hongguang Pan;Zhuoyi Li;Yunpeng Fu;Xuebin Qin;Jianchen Hu","doi":"10.1109/THMS.2024.3407875","DOIUrl":"10.1109/THMS.2024.3407875","url":null,"abstract":"Reconstructing visual stimulus representation is a significant task in neural decoding. Until now, most studies have considered functional magnetic resonance imaging (fMRI) as the signal source. However, fMRI-based image reconstruction methods are challenging to apply widely due to the complexity and high cost of acquisition equipment. Taking into account the advantages of the low cost and easy portability of electroencephalogram (EEG) acquisition equipment, we propose a novel image reconstruction method based on EEG signals in this article. First, to meet the high recognizability of visual stimulus images in a fast-switching manner, we construct a visual stimuli image dataset and obtain the corresponding EEG dataset through EEG signals collection experiment. Second, we introduce the deep visual representation model (DVRM), comprising a primary encoder and a subordinate decoder, to reconstruct visual stimuli representation. The encoder is designed based on residual-in-residual dense blocks to learn the distribution characteristics between EEG signals and visual stimulus images. Meanwhile, the decoder is designed using a deep neural network to reconstruct the visual stimulus representation from the learned deep visual representation. The DVRM can accommodate the deep and multiview visual features of the human natural state, resulting in more precise reconstructed images. Finally, we evaluate the DVRM based on the quality of the generated images using our EEG dataset. The results demonstrate that the DVRM exhibits an excellent performance in learning deep visual representation from EEG signals, generating reconstructed representation of images that are realistic and highly resemble the original images.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 6","pages":"711-722"},"PeriodicalIF":3.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-18DOI: 10.1109/THMS.2024.3450831
Maurice Kolff;Joost Venrooij;Markus Schwienbacher;Daan M. Pool;Max Mulder
In moving-base driving simulators, the sensation of the inertial car motion provided by the motion system is controlled by the motion cueing algorithm (MCA). Due to the difficulty of reproducing the inertial motion in urban simulations, accurate prediction tools for subjective evaluation of the simulator's inertial motion are required. In this article, an open-loop driving experiment in an urban scenario is discussed, in which 60 participants evaluated the motion cueing through an overall rating and a continuous rating method. Three MCAs were tested that represent different levels of motion cueing quality. It is investigated under which conditions the continuous rating method provides reliable data in urban scenarios through the estimation of Cronbach's alpha and McDonald's omega. Results show that the better the motion cueing is rated, the lower the reliability of that rating data is, and the less the continuous rating and overall rating correlate. This suggests that subjective ratings for motion quality are dominated by (moments of) incongruent motion, while congruent motion is less important. Furthermore, through a forward regression approach, it is shown that participants' rating behavior can be described by a first-order low-pass filtered response to the lateral specific force mismatch (66.0%), as well as a similar response to the longitudinal specific force mismatch (34.0%). By this better understanding of the acquired ratings in urban driving simulations, including their reliability and predictability, incongruences can be more accurately targeted and reduced.
{"title":"Reliability and Models of Subjective Motion Incongruence Ratings in Urban Driving Simulations","authors":"Maurice Kolff;Joost Venrooij;Markus Schwienbacher;Daan M. Pool;Max Mulder","doi":"10.1109/THMS.2024.3450831","DOIUrl":"10.1109/THMS.2024.3450831","url":null,"abstract":"In moving-base driving simulators, the sensation of the inertial car motion provided by the motion system is controlled by the motion cueing algorithm (MCA). Due to the difficulty of reproducing the inertial motion in urban simulations, accurate prediction tools for subjective evaluation of the simulator's inertial motion are required. In this article, an open-loop driving experiment in an urban scenario is discussed, in which 60 participants evaluated the motion cueing through an overall rating and a continuous rating method. Three MCAs were tested that represent different levels of motion cueing quality. It is investigated under which conditions the continuous rating method provides reliable data in urban scenarios through the estimation of Cronbach's alpha and McDonald's omega. Results show that the \u0000<italic>better</i>\u0000 the motion cueing is rated, the \u0000<italic>lower</i>\u0000 the reliability of that rating data is, and the less the continuous rating and overall rating correlate. This suggests that subjective ratings for motion quality are dominated by (moments of) incongruent motion, while congruent motion is less important. Furthermore, through a forward regression approach, it is shown that participants' rating behavior can be described by a first-order low-pass filtered response to the lateral specific force mismatch (66.0%), as well as a similar response to the longitudinal specific force mismatch (34.0%). By this better understanding of the acquired ratings in urban driving simulations, including their reliability and predictability, incongruences can be more accurately targeted and reduced.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 6","pages":"634-645"},"PeriodicalIF":3.5,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Trust is crucial for ensuring the safety, security, and widespread adoption of automated vehicles (AVs), and if trust is lacking, drivers and the general public may hesitate to embrace this technology. This research seeks to investigate contextualized trust profiles in order to create personalized experiences for drivers in AVs with varying levels of reliability. A driving simulator experiment involving 70 participants revealed three distinct contextualized trust profiles (i.e., confident copilots , myopic pragmatists , and reluctant automators ) identified through K-means clustering, and analyzed in relation to drivers' dynamic trust, dispositional trust, initial learned trust, personality traits, and emotions. The experiment encompassed eight scenarios where participants were requested to take over control from the AV in three conditions: a control condition, a false alarm condition, and a miss condition. To validate the models, a multinomial logistic regression model was constructed using the shapley additive explanations explainer to determine the most influential features in predicting contextualized trust profiles, achieving an F1-score of 0.90 and an accuracy of 0.89. In addition, an examination of how individual factors impact contextualized trust profiles provided valuable insights into trust dynamics from a user-centric perspective. The outcomes of this research hold significant implications for the development of personalized in-vehicle trust monitoring and calibration systems to modulate drivers' trust levels, thereby enhancing safety and user experience in automated driving.
信任对于确保自动驾驶汽车（AVs）的安全可靠和广泛应用至关重要，如果缺乏信任，驾驶员和公众可能会对接受这项技术犹豫不决。本研究旨在调查情境化的信任特征，以便为驾驶者在不同可靠性水平的自动驾驶汽车中创造个性化体验。有 70 名参与者参加的驾驶模拟器实验通过 K-means 聚类发现了三种不同的情境化信任特征（即自信的副驾驶、近视的实用主义者和不情愿的自动驾驶者），并对驾驶者的动态信任、处置信任、初始学习信任、个性特征和情绪进行了分析。实验包括八个场景，要求参与者在三个条件下接管自动驾驶汽车的控制权：控制条件、误报条件和错过条件。为了验证模型，我们使用夏普利加法解释器构建了一个多二项逻辑回归模型，以确定在预测情境化信任档案时最有影响力的特征，该模型的 F1 分数为 0.90，准确率为 0.89。此外，对个体因素如何影响情境化信任档案的研究从以用户为中心的角度为信任动态提供了宝贵的见解。这项研究的成果对开发个性化车载信任监控和校准系统具有重要意义，可调节驾驶员的信任水平，从而提高自动驾驶的安全性和用户体验。
{"title":"Building Contextualized Trust Profiles in Conditionally Automated Driving","authors":"Lilit Avetisyan;Jackie Ayoub;X. Jessie Yang;Feng Zhou","doi":"10.1109/THMS.2024.3452411","DOIUrl":"10.1109/THMS.2024.3452411","url":null,"abstract":"Trust is crucial for ensuring the safety, security, and widespread adoption of automated vehicles (AVs), and if trust is lacking, drivers and the general public may hesitate to embrace this technology. This research seeks to investigate contextualized trust profiles in order to create personalized experiences for drivers in AVs with varying levels of reliability. A driving simulator experiment involving 70 participants revealed three distinct contextualized trust profiles (i.e., \u0000<italic>confident copilots</i>\u0000, \u0000<italic>myopic pragmatists</i>\u0000, and \u0000<italic>reluctant automators</i>\u0000) identified through K-means clustering, and analyzed in relation to drivers' dynamic trust, dispositional trust, initial learned trust, personality traits, and emotions. The experiment encompassed eight scenarios where participants were requested to take over control from the AV in three conditions: a control condition, a false alarm condition, and a miss condition. To validate the models, a multinomial logistic regression model was constructed using the shapley additive explanations explainer to determine the most influential features in predicting contextualized trust profiles, achieving an F1-score of 0.90 and an accuracy of 0.89. In addition, an examination of how individual factors impact contextualized trust profiles provided valuable insights into trust dynamics from a user-centric perspective. The outcomes of this research hold significant implications for the development of personalized in-vehicle trust monitoring and calibration systems to modulate drivers' trust levels, thereby enhancing safety and user experience in automated driving.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 6","pages":"658-667"},"PeriodicalIF":3.5,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142268869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-12DOI: 10.1109/THMS.2024.3436876
Chunxi Huang;Jiyao Wang;Song Yan;Dengbo He
Drivers’ appropriate mental models of and trust in advanced driver assistance systems (ADAS) are essential to driving safety in vehicles with ADAS. Although several previous studies evaluated drivers’ ADAS mental models of and trust in adaptive cruise control and lane-keeping assist systems, research gaps still exist. Specifically, recent developments in ADAS have made more advanced functions available but they have been under-investigated. Furthermore, the widely adopted proportional correctness-based scores may not differentiate drivers’ objective ADAS mental model and subjective bias toward the ADAS. Finally, most previous studies adopted only regression models to explore the influential factors and thus may have ignored the underlying association among the factors. Therefore, our study aimed to explore drivers’ mental models of and trust in emerging ADAS by using the sensitivity (i.e., d’ ) and response bias (i.e., c ) measures from the signal detection theory. We modeled the data from 287 drivers using additive Bayesian network (ABN) and further interpreted the graph model using regression analysis. We found that different factors might be associated with drivers’ objective knowledge of ADAS and subjective bias toward the existence of functions/limitations. Furthermore, drivers’ subjective bias was more associated with their trust in ADAS compared to objective knowledge. The findings from our study provide new insights into the influential factors on drivers’ mental models of ADAS and better reveal how mental models can affect trust in ADAS. It also provides a case study on how the mixed approach with ABN and regression analysis can model observational data.
{"title":"Exploring Factors Related to Drivers’ Mental Model of and Trust in Advanced Driver Assistance Systems Using an ABN-Based Mixed Approach","authors":"Chunxi Huang;Jiyao Wang;Song Yan;Dengbo He","doi":"10.1109/THMS.2024.3436876","DOIUrl":"10.1109/THMS.2024.3436876","url":null,"abstract":"Drivers’ appropriate mental models of and trust in advanced driver assistance systems (ADAS) are essential to driving safety in vehicles with ADAS. Although several previous studies evaluated drivers’ ADAS mental models of and trust in adaptive cruise control and lane-keeping assist systems, research gaps still exist. Specifically, recent developments in ADAS have made more advanced functions available but they have been under-investigated. Furthermore, the widely adopted proportional correctness-based scores may not differentiate drivers’ objective ADAS mental model and subjective bias toward the ADAS. Finally, most previous studies adopted only regression models to explore the influential factors and thus may have ignored the underlying association among the factors. Therefore, our study aimed to explore drivers’ mental models of and trust in emerging ADAS by using the sensitivity (i.e., \u0000<italic>d’</i>\u0000) and response bias (i.e., \u0000<italic>c</i>\u0000) measures from the signal detection theory. We modeled the data from 287 drivers using additive Bayesian network (ABN) and further interpreted the graph model using regression analysis. We found that different factors might be associated with drivers’ objective knowledge of ADAS and subjective bias toward the existence of functions/limitations. Furthermore, drivers’ subjective bias was more associated with their trust in ADAS compared to objective knowledge. The findings from our study provide new insights into the influential factors on drivers’ mental models of ADAS and better reveal how mental models can affect trust in ADAS. It also provides a case study on how the mixed approach with ABN and regression analysis can model observational data.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 6","pages":"646-657"},"PeriodicalIF":3.5,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The field of human motion prediction has gained prominence, finding applications in various domains such as intelligent surveillance and human–robot interaction. However, predicting full-body human motion poses challenges in capturing joint interactions, handling diverse movement patterns, managing occlusions, and ensuring real-time performance. To address these challenges, the proposed model adopts a skeleton-parted strategy to dissect the skeleton structure, enhancing coordination and fusion between body parts. This novel method combines transformer-enabled graph convolutional networks for predicting human motion in 3-D skeleton data. It integrates a temporal transformer (T-Transformer) for comprehensive temporal feature extraction and a spatial graph convolutional network (S-GCN) for capturing spatial characteristics of human motion. The model's performance is evaluated on two comprehensive human motion datasets, Human3.6M and CMU motion capture (CMU Mocap), containing numerous videos encompassing short and long human motion sequences. Results indicate that the proposed model outperforms state-of-the-art methods on both datasets, significantly improving the average mean per joint positional error (avg-MPJPE) by 3.50% and 11.45% for short-term and long-term motion prediction, respectively. Similarly, on the CMU Mocap dataset, it achieves avg-MPJPE improvements of 2.69% and 1.05% for short-term and long-term motion prediction, respectively, demonstrating its superior accuracy in predicting human motion over extended periods. The study also investigates the impact of different numbers of T-Transformers and S-GCNs and explores the specific roles and contributions of the T-Transformer, S-GCN, and cross-part components.
{"title":"Fusion of Temporal Transformer and Spatial Graph Convolutional Network for 3-D Skeleton-Parts-Based Human Motion Prediction","authors":"Mayank Lovanshi;Vivek Tiwari;Rajesh Ingle;Swati Jain","doi":"10.1109/THMS.2024.3452133","DOIUrl":"10.1109/THMS.2024.3452133","url":null,"abstract":"The field of human motion prediction has gained prominence, finding applications in various domains such as intelligent surveillance and human–robot interaction. However, predicting full-body human motion poses challenges in capturing joint interactions, handling diverse movement patterns, managing occlusions, and ensuring real-time performance. To address these challenges, the proposed model adopts a skeleton-parted strategy to dissect the skeleton structure, enhancing coordination and fusion between body parts. This novel method combines transformer-enabled graph convolutional networks for predicting human motion in 3-D skeleton data. It integrates a temporal transformer (T-Transformer) for comprehensive temporal feature extraction and a spatial graph convolutional network (S-GCN) for capturing spatial characteristics of human motion. The model's performance is evaluated on two comprehensive human motion datasets, Human3.6M and CMU motion capture (CMU Mocap), containing numerous videos encompassing short and long human motion sequences. Results indicate that the proposed model outperforms state-of-the-art methods on both datasets, significantly improving the average mean per joint positional error (avg-MPJPE) by 3.50% and 11.45% for short-term and long-term motion prediction, respectively. Similarly, on the CMU Mocap dataset, it achieves avg-MPJPE improvements of 2.69% and 1.05% for short-term and long-term motion prediction, respectively, demonstrating its superior accuracy in predicting human motion over extended periods. The study also investigates the impact of different numbers of T-Transformers and S-GCNs and explores the specific roles and contributions of the T-Transformer, S-GCN, and cross-part components.","PeriodicalId":48916,"journal":{"name":"IEEE Transactions on Human-Machine Systems","volume":"54 6","pages":"788-797"},"PeriodicalIF":3.5,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142199163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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