Pub Date : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209493
Qiannan Han, P. Nesi, G. Pantaleo, I. Paoli
A smart city dashboard is an application that is capable of retrieving historical and real-time data from different sources and visualizing data through different graphical and interactive techniques, and also to act on data storage and IOT devices. To ensure interoperability with various IoT protocols and devices, we have developed Snap4City Dashboard Builder, one-of-a-kind tool that is then used to create multiple smart city dashboards. This paper briefly discusses the tool we developed and presents a thorough evaluation of the tool, followed by description of ten dashboards created using the builder and summary of our extensive assessment of these dashboards. The evaluation indicates that our dashboard builder is powerful and the dashboards are very helpful for city officials.
{"title":"Smart City Dashboards: Design, Development, and Evaluation","authors":"Qiannan Han, P. Nesi, G. Pantaleo, I. Paoli","doi":"10.1109/ICHMS49158.2020.9209493","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209493","url":null,"abstract":"A smart city dashboard is an application that is capable of retrieving historical and real-time data from different sources and visualizing data through different graphical and interactive techniques, and also to act on data storage and IOT devices. To ensure interoperability with various IoT protocols and devices, we have developed Snap4City Dashboard Builder, one-of-a-kind tool that is then used to create multiple smart city dashboards. This paper briefly discusses the tool we developed and presents a thorough evaluation of the tool, followed by description of ten dashboards created using the builder and summary of our extensive assessment of these dashboards. The evaluation indicates that our dashboard builder is powerful and the dashboards are very helpful for city officials.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127111176","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 : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209557
Francesco Zinnari, S. Strada, M. Tanelli, S. Formentin, Gianluca Savaia, S. Savaresi
Electric Vehicles (EVs) are quickly becoming a very important segment of the automotive industry. However, the so-called range anxiety, i.e., the fear that a vehicle has insufficient range to reach its destination, the experience anxiety, i.e.,the fear of the hassle of public charging and the high selling price are still major barriers to a widespread adoption of electric cars. In this paper, we use real-world data from vehicle telematics devices to quantitatively assess whether range anxiety is a justified threat. Specifically, we evaluate the vehicles electrification potential based on their real driving patterns, showing that a significant percentage of traditional Internal Combustion Engine (ICE) Vehicles could be effortlessly replaced by EVs, without any impact on the owners’ driving habits and with the current public charging infrastructure, only ensuring an overnight recharging.
{"title":"Mining the electrification potential of fuel-based vehicles mobility patterns: a data-based approach","authors":"Francesco Zinnari, S. Strada, M. Tanelli, S. Formentin, Gianluca Savaia, S. Savaresi","doi":"10.1109/ICHMS49158.2020.9209557","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209557","url":null,"abstract":"Electric Vehicles (EVs) are quickly becoming a very important segment of the automotive industry. However, the so-called range anxiety, i.e., the fear that a vehicle has insufficient range to reach its destination, the experience anxiety, i.e.,the fear of the hassle of public charging and the high selling price are still major barriers to a widespread adoption of electric cars. In this paper, we use real-world data from vehicle telematics devices to quantitatively assess whether range anxiety is a justified threat. Specifically, we evaluate the vehicles electrification potential based on their real driving patterns, showing that a significant percentage of traditional Internal Combustion Engine (ICE) Vehicles could be effortlessly replaced by EVs, without any impact on the owners’ driving habits and with the current public charging infrastructure, only ensuring an overnight recharging.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"5 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126973508","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 : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209377
Lingqiu Jin, He Zhang, Yantao Shen, C. Ye
This paper presents a new hand-worn device, called wearable robotic object manipulation aid (W-ROMA), that can help a visually impaired individual locate a target object and guide the hand to take a hold of it. W-ROMA may assist the individual for navigational (e.g., grasping a chair and moving it to make path) or non-navigational purpose (e.g, grasping a mug). The device consists of a sensing unit and a guiding unit. The sensing unit uses a Structure Core sensor, comprising of an RGB-D camera and an Inertial Measurement Unit (IMU), to detect the target object and estimate the device pose. Based on the object and pose information, the guiding unit computes the Desired Hand Movement (DHM) and convey it to the user by an electro-tactile display to guide the hand to approach the object. A speech interface is developed and used as an additional way to convey the DHM and used for human-robot interaction. A new method, called Depth Enhanced Visual-Inertial Odometry (DVIO), is proposed for 6-DOF device pose estimation. It tightly couples the camera’s depth and visual data with the IMU data in a graph optimization process to produce more accurate pose estimation than the existing state-of-the-art approach. The estimated poses are used to “stitched” the imaging and point cloud data captured at different points to form a larger view of the scene for object detection. They can also be used to position the individual for wayfinding. Experimental results demonstrate that the DVIO method outperforms the state-of-the-art VIO approach in 6-DOF pose estimation.
{"title":"Human-Robot Interaction for Assisted Object Grasping by a Wearable Robotic Object Manipulation Aid for the Blind","authors":"Lingqiu Jin, He Zhang, Yantao Shen, C. Ye","doi":"10.1109/ICHMS49158.2020.9209377","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209377","url":null,"abstract":"This paper presents a new hand-worn device, called wearable robotic object manipulation aid (W-ROMA), that can help a visually impaired individual locate a target object and guide the hand to take a hold of it. W-ROMA may assist the individual for navigational (e.g., grasping a chair and moving it to make path) or non-navigational purpose (e.g, grasping a mug). The device consists of a sensing unit and a guiding unit. The sensing unit uses a Structure Core sensor, comprising of an RGB-D camera and an Inertial Measurement Unit (IMU), to detect the target object and estimate the device pose. Based on the object and pose information, the guiding unit computes the Desired Hand Movement (DHM) and convey it to the user by an electro-tactile display to guide the hand to approach the object. A speech interface is developed and used as an additional way to convey the DHM and used for human-robot interaction. A new method, called Depth Enhanced Visual-Inertial Odometry (DVIO), is proposed for 6-DOF device pose estimation. It tightly couples the camera’s depth and visual data with the IMU data in a graph optimization process to produce more accurate pose estimation than the existing state-of-the-art approach. The estimated poses are used to “stitched” the imaging and point cloud data captured at different points to form a larger view of the scene for object detection. They can also be used to position the individual for wayfinding. Experimental results demonstrate that the DVIO method outperforms the state-of-the-art VIO approach in 6-DOF pose estimation.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130507356","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 : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209543
S. Cooper, Samuel F.P. Fensome, D. Kourtis, Stuart Gow, M. Dragone
Human-robot joint action is a key requirement in many advanced robotic applications where robots are not only expected to work alongside humans but also collaborate with them in the performance of physical tasks. Robots are already programmed to model and predict human actions in order to ensure smooth collaboration and overall task efficiency. However, little is known on how humans represent and account for robot’s actions as part of their own plans. This paper presents a first joint psychological and HRI user study designed to answer this question in the context of human-robot handover scenarios. Our analysis showed that the participants had a positive userexperience of the interaction and adopted gaze patterns similar to a large extent to the ones in human-to-human handover tasks. The EEG analysis suggests that, compared to solo action, the human participants were at a state of higher motor readiness when they prepared to hand over the object to the robot either because they represented the robot’s action in advance or alternatively that they anticipated that passing the object to the robot would be a more effortful action, thus highlighting the increased demands in planning a human-to-robot interaction. Our findings highlight the value of gaze as a positive method of non-verbal communication in HRI and provides new insights in the neural mechanisms that allows a person to plan an effective interaction with a robot.
{"title":"An EEG investigation on planning human-robot handover tasks","authors":"S. Cooper, Samuel F.P. Fensome, D. Kourtis, Stuart Gow, M. Dragone","doi":"10.1109/ICHMS49158.2020.9209543","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209543","url":null,"abstract":"Human-robot joint action is a key requirement in many advanced robotic applications where robots are not only expected to work alongside humans but also collaborate with them in the performance of physical tasks. Robots are already programmed to model and predict human actions in order to ensure smooth collaboration and overall task efficiency. However, little is known on how humans represent and account for robot’s actions as part of their own plans. This paper presents a first joint psychological and HRI user study designed to answer this question in the context of human-robot handover scenarios. Our analysis showed that the participants had a positive userexperience of the interaction and adopted gaze patterns similar to a large extent to the ones in human-to-human handover tasks. The EEG analysis suggests that, compared to solo action, the human participants were at a state of higher motor readiness when they prepared to hand over the object to the robot either because they represented the robot’s action in advance or alternatively that they anticipated that passing the object to the robot would be a more effortful action, thus highlighting the increased demands in planning a human-to-robot interaction. Our findings highlight the value of gaze as a positive method of non-verbal communication in HRI and provides new insights in the neural mechanisms that allows a person to plan an effective interaction with a robot.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124926569","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 : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209537
Fabio Angellotti, G. Costagliola, Mattia De Rosa, V. Fuccella
We present the design and evaluation of a pointing method for disabled people, called iFree, which can run on mobile devices such as smartphones and tablets. The method is based on face recognition and supports target selection (click) in two different modes: by opening and closing the mouth (mouth click) and by making a sound (sound click).The method was experimented on a smartphone with 14 non-disabled participants in order to evaluate its performance and to compare the two above mentioned modes. In particular, we measured its performance using ISO 9241-411 standard task. The throughput grand mean was 0.86 bit/s. The sound click mode had a greater throughput and accuracy, a lower movement time and was preferred by participants.
{"title":"iFree: design and evaluation of a pointing method for disabled users on mobile devices","authors":"Fabio Angellotti, G. Costagliola, Mattia De Rosa, V. Fuccella","doi":"10.1109/ICHMS49158.2020.9209537","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209537","url":null,"abstract":"We present the design and evaluation of a pointing method for disabled people, called iFree, which can run on mobile devices such as smartphones and tablets. The method is based on face recognition and supports target selection (click) in two different modes: by opening and closing the mouth (mouth click) and by making a sound (sound click).The method was experimented on a smartphone with 14 non-disabled participants in order to evaluate its performance and to compare the two above mentioned modes. In particular, we measured its performance using ISO 9241-411 standard task. The throughput grand mean was 0.86 bit/s. The sound click mode had a greater throughput and accuracy, a lower movement time and was preferred by participants.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125284822","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 : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209496
Tatsuya Otoshi, S. Arakawa, M. Murata, Kai Wang, T. Hosomi, Toshiyuki Kanoh
In the IoT, AI should automatically learn the new categories when the environment changes caused by moving, adding, or removing the devices. In literature, several methods are proposed for learning in a new environment such as selftraining and transfer learning. However, there are yet some issues to learn the new category related to small data for the new category and noise existence. On the other hand, noise-tolerant models have been developed such as the Bayesian Attractor Model (BAM) which is a cognitive model for decision making under uncertainty of information. In this paper, we develop a system to learn the new category automatically by extending the BAM. Using the BAM as a classifier, the system makes the temporal classifier with new observation and collects the training data for the new category. Through the evaluation, we show that our method is superior to the neural network approach in the scenario of learning new categories.
{"title":"Acquiring New Categories by Self Data Gathering with Bayesian Attractor Model","authors":"Tatsuya Otoshi, S. Arakawa, M. Murata, Kai Wang, T. Hosomi, Toshiyuki Kanoh","doi":"10.1109/ICHMS49158.2020.9209496","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209496","url":null,"abstract":"In the IoT, AI should automatically learn the new categories when the environment changes caused by moving, adding, or removing the devices. In literature, several methods are proposed for learning in a new environment such as selftraining and transfer learning. However, there are yet some issues to learn the new category related to small data for the new category and noise existence. On the other hand, noise-tolerant models have been developed such as the Bayesian Attractor Model (BAM) which is a cognitive model for decision making under uncertainty of information. In this paper, we develop a system to learn the new category automatically by extending the BAM. Using the BAM as a classifier, the system makes the temporal classifier with new observation and collects the training data for the new category. Through the evaluation, we show that our method is superior to the neural network approach in the scenario of learning new categories.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131394748","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 : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209325
M. Lopes, Carl Vogel
Growing interest in the impact of gender on technology includes concerns with regard to gender bias and its implication in the user experience. Past explorations of gender and design revealed significant differences between female and male users with regard to the perception and the acceptance of dating apps. A novel study was designed to investigate whether gender differences in the user experience could be caused by gender bias in mobile app development. Significant differences in the definition of design priorities emerged with regard to the participants’ gender and the persona’s perceived gender.
{"title":"Gender Effects in Mobile Application Development","authors":"M. Lopes, Carl Vogel","doi":"10.1109/ICHMS49158.2020.9209325","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209325","url":null,"abstract":"Growing interest in the impact of gender on technology includes concerns with regard to gender bias and its implication in the user experience. Past explorations of gender and design revealed significant differences between female and male users with regard to the perception and the acceptance of dating apps. A novel study was designed to investigate whether gender differences in the user experience could be caused by gender bias in mobile app development. Significant differences in the definition of design priorities emerged with regard to the participants’ gender and the persona’s perceived gender.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"151 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121781714","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 : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209397
Filippo Cantucci, R. Falcone
Cooperation between autonomous robots and humans is becoming more and more demanding. Robots have to be able to capable of possessing and expose a wide range of cognitive functions, once humans require their help. This paper describes a cognitive architecture for human-robot interaction that allows a robot to dynamically modulate its own level of social autonomy every time a human user delegates to it a task to accomplish in her/his place. The task adoption process leverages on multiple robot’s cognitive capabilities (i.e. the ability to have a theory of mind of the user, to build a model of the world, to profile the user and to make an evaluation about its own skill trustworthiness for building the user’s profile). On the basis of these capabilities the robot is able to adapt its own level of intelligent collaboration by adopting the task at the different levels of help defined in the theory of delegation and adoption conceived by Castelfranchi and Falcone. Besides that, the architecture enhances robot’s behavior transparency because gives to it the ability to provide a comprehensive explanation of the strategy it has adopted for accomplishing the delegated task. We propose an implementation of the cognitive architecture based on JaCaMo framework, which provides support for implementing multi-agent systems and integrates different multi-agent programming dimensions.
{"title":"Towards trustworthiness and transparency in social human-robot interaction","authors":"Filippo Cantucci, R. Falcone","doi":"10.1109/ICHMS49158.2020.9209397","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209397","url":null,"abstract":"Cooperation between autonomous robots and humans is becoming more and more demanding. Robots have to be able to capable of possessing and expose a wide range of cognitive functions, once humans require their help. This paper describes a cognitive architecture for human-robot interaction that allows a robot to dynamically modulate its own level of social autonomy every time a human user delegates to it a task to accomplish in her/his place. The task adoption process leverages on multiple robot’s cognitive capabilities (i.e. the ability to have a theory of mind of the user, to build a model of the world, to profile the user and to make an evaluation about its own skill trustworthiness for building the user’s profile). On the basis of these capabilities the robot is able to adapt its own level of intelligent collaboration by adopting the task at the different levels of help defined in the theory of delegation and adoption conceived by Castelfranchi and Falcone. Besides that, the architecture enhances robot’s behavior transparency because gives to it the ability to provide a comprehensive explanation of the strategy it has adopted for accomplishing the delegated task. We propose an implementation of the cognitive architecture based on JaCaMo framework, which provides support for implementing multi-agent systems and integrates different multi-agent programming dimensions.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114166358","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 : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209455
Haibin Zhu
Social simulation is a cutting edge topic in an interdisciplinary field where Information Technology and Social Science overlap. In this paper, we set out the fundamental requirements for social simulation systems and demonstrate that Role-Based Collaboration (RBC) and the Environments – Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is highly qualified to meet these requirements.As a case study, peer review is a typical social process, a part of which can be modeled as a role assignment problem. After we present the basics of a current typical peer review process, we apply RBC and E-CARGO model to renovate the process. Simulation experiments show the benefits of this improvement.
{"title":"Social Simulation with RBC","authors":"Haibin Zhu","doi":"10.1109/ICHMS49158.2020.9209455","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209455","url":null,"abstract":"Social simulation is a cutting edge topic in an interdisciplinary field where Information Technology and Social Science overlap. In this paper, we set out the fundamental requirements for social simulation systems and demonstrate that Role-Based Collaboration (RBC) and the Environments – Classes, Agents, Roles, Groups, and Objects (E-CARGO) model is highly qualified to meet these requirements.As a case study, peer review is a typical social process, a part of which can be modeled as a role assignment problem. After we present the basics of a current typical peer review process, we apply RBC and E-CARGO model to renovate the process. Simulation experiments show the benefits of this improvement.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117130977","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 : 2020-09-01DOI: 10.1109/ICHMS49158.2020.9209461
Guy Cohen-Lazry, Ariel Telpaz, A. Degani, T. Oron-Gilad
The present study aimed at exploring the factors that induce discomfort when riding automated vehicles in various driving events. Using a Wizard-of-Oz design, participants were driven in what appeared to be a fully automated (level 5) vehicle. An analysis of participants’ reactions and verbal utterances revealed six specific sources of discomfort (Vehicle’s speed, Crossing Traffic, Pedestrians, Road Geometry, Road Markings and On-coming Traffic). These discomfort reactions were then cross-referenced to the road events they were associated with. The results suggest that there is a different set of discomfort sources associated with each unique road event. Overall, we identified seven distinct categories of road events that could cover the entire experimental route (Crosswalks, Roundabouts, Straight Intersections, Left-Turns with On-coming Traffic, Left-Turns without On-coming Traffic, Right turns with Right-of-way and Right turns without Right-of-way). From a design perspective, these findings can empower the development of interventions to reduce discomfort in automated vehicles.
{"title":"Identifying Sources of Discomfort in Various Road Events While Riding Automated Vehicles","authors":"Guy Cohen-Lazry, Ariel Telpaz, A. Degani, T. Oron-Gilad","doi":"10.1109/ICHMS49158.2020.9209461","DOIUrl":"https://doi.org/10.1109/ICHMS49158.2020.9209461","url":null,"abstract":"The present study aimed at exploring the factors that induce discomfort when riding automated vehicles in various driving events. Using a Wizard-of-Oz design, participants were driven in what appeared to be a fully automated (level 5) vehicle. An analysis of participants’ reactions and verbal utterances revealed six specific sources of discomfort (Vehicle’s speed, Crossing Traffic, Pedestrians, Road Geometry, Road Markings and On-coming Traffic). These discomfort reactions were then cross-referenced to the road events they were associated with. The results suggest that there is a different set of discomfort sources associated with each unique road event. Overall, we identified seven distinct categories of road events that could cover the entire experimental route (Crosswalks, Roundabouts, Straight Intersections, Left-Turns with On-coming Traffic, Left-Turns without On-coming Traffic, Right turns with Right-of-way and Right turns without Right-of-way). From a design perspective, these findings can empower the development of interventions to reduce discomfort in automated vehicles.","PeriodicalId":132917,"journal":{"name":"2020 IEEE International Conference on Human-Machine Systems (ICHMS)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114993298","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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