{"title":"Modeling intent and destination prediction within a Bayesian framework: Predictive touch as a usecase","authors":"Runze Gan, Jiaming Liang, B. I. Ahmad, S. Godsill","doi":"10.1017/dce.2020.11","DOIUrl":null,"url":null,"abstract":"Abstract In various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestrian, animal, vehicle, and others, is driven by achieving a premeditated goal such as reaching a destination. This is albeit the various possible trajectories to this endpoint. This paper presents a generic Bayesian framework that utilizes stochastic models that can capture the influence of intent (viz., destination) on the object behavior. It leads to simple algorithms to infer, as early as possible, the intended endpoint from noisy sensory observations, with relatively low computational and training data requirements. This framework is introduced in the context of the novel predictive touch technology for intelligent user interfaces and touchless interactions. It can determine, early in the interaction task or pointing gesture, the interface item the user intends to select on the display (e.g., touchscreen) and accordingly simplify as well as expedite the selection task. This is shown to significantly improve the usability of displays in vehicles, especially under the influence of perturbations due to road and driving conditions, and enable intuitive contact-free interactions. Data collected in instrumented vehicles are shown to demonstrate the effectiveness of the proposed intent prediction approach. Impact Statement The presented Bayesian framework facilitates automated decision-making, resource allocation and future action planning with applications in various fields, such as in human–computer interaction (HCI), surveillance, robotics, to name a few. It led to the introduction of the patented HCI technology predictive touch, developed as part of a collaboration with Jaguar Land Rover and is set for commercialization; it won a Jaguar Land Rover TATA Innovista Award 2020 (“Dare To Try” category). Predictive touch does not only offer an intuitive approach to touchless interactions (i.e., no physical contact with the display is required), but also it can significantly improve the usability of interactive displays in vehicles or any moving platform, reduce the attention they require and enhance the input accuracy, including under the influence of perturbations due to road and driving conditions. This has been demonstrated in various on-road trials. This touchless interaction technology can have widespread applications in a post COVID-19 world by minimizing the risk of transmission of pathogens via touch surfaces, for instance, when using ticketing or self checkout machines, control panels, and interactive displays in public spaces, kiosks, or workplaces, and so on. It also offers a means to easily interact with emerging display technologies that do not have a physical surface, such as 2D/3D projections and in virtual or augmented reality, and offers additional design flexibility to support inclusive design practices.","PeriodicalId":34169,"journal":{"name":"DataCentric Engineering","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2020-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/dce.2020.11","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"DataCentric Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1017/dce.2020.11","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 7
Abstract
Abstract In various scenarios, the motion of a tracked object, for example, a pointing apparatus, pedestrian, animal, vehicle, and others, is driven by achieving a premeditated goal such as reaching a destination. This is albeit the various possible trajectories to this endpoint. This paper presents a generic Bayesian framework that utilizes stochastic models that can capture the influence of intent (viz., destination) on the object behavior. It leads to simple algorithms to infer, as early as possible, the intended endpoint from noisy sensory observations, with relatively low computational and training data requirements. This framework is introduced in the context of the novel predictive touch technology for intelligent user interfaces and touchless interactions. It can determine, early in the interaction task or pointing gesture, the interface item the user intends to select on the display (e.g., touchscreen) and accordingly simplify as well as expedite the selection task. This is shown to significantly improve the usability of displays in vehicles, especially under the influence of perturbations due to road and driving conditions, and enable intuitive contact-free interactions. Data collected in instrumented vehicles are shown to demonstrate the effectiveness of the proposed intent prediction approach. Impact Statement The presented Bayesian framework facilitates automated decision-making, resource allocation and future action planning with applications in various fields, such as in human–computer interaction (HCI), surveillance, robotics, to name a few. It led to the introduction of the patented HCI technology predictive touch, developed as part of a collaboration with Jaguar Land Rover and is set for commercialization; it won a Jaguar Land Rover TATA Innovista Award 2020 (“Dare To Try” category). Predictive touch does not only offer an intuitive approach to touchless interactions (i.e., no physical contact with the display is required), but also it can significantly improve the usability of interactive displays in vehicles or any moving platform, reduce the attention they require and enhance the input accuracy, including under the influence of perturbations due to road and driving conditions. This has been demonstrated in various on-road trials. This touchless interaction technology can have widespread applications in a post COVID-19 world by minimizing the risk of transmission of pathogens via touch surfaces, for instance, when using ticketing or self checkout machines, control panels, and interactive displays in public spaces, kiosks, or workplaces, and so on. It also offers a means to easily interact with emerging display technologies that do not have a physical surface, such as 2D/3D projections and in virtual or augmented reality, and offers additional design flexibility to support inclusive design practices.