Modeling intent and destination prediction within a Bayesian framework: Predictive touch as a usecase

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Runze Gan, Jiaming Liang, B. I. Ahmad, S. Godsill
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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.
贝叶斯框架下的意图和目的地预测建模:预测触摸作为一个用例
摘要在各种场景中,被跟踪物体的运动,例如指向装置、行人、动物、车辆和其他物体,是通过实现预定目标(如到达目的地)来驱动的。尽管这是到达这个终点的各种可能的轨迹。本文提出了一个通用的贝叶斯框架,该框架利用随机模型来捕捉意图(即目的地)对对象行为的影响。它导致了一种简单的算法,可以从嘈杂的感官观察中尽早推断出预期的终点,并且对计算和训练数据的要求相对较低。该框架是在用于智能用户界面和无接触交互的新型预测触摸技术的背景下引入的。它可以在交互任务或指示手势的早期确定用户打算在显示器(例如,触摸屏)上选择的界面项目,并相应地简化和加快选择任务。这被证明显著提高了车辆中显示器的可用性,特别是在道路和驾驶条件引起的扰动的影响下,并实现了直观的无接触交互。在装有仪器的车辆中收集的数据表明了所提出的意图预测方法的有效性。影响声明所提出的贝叶斯框架有助于自动化决策、资源分配和未来行动规划,应用于各个领域,如人机交互(HCI)、监控、机器人等。这导致了HCI专利技术预测触摸的引入,该技术是与捷豹路虎合作开发的,并将商业化;它获得了2020年捷豹路虎TATA Innovista奖(“敢于尝试”类别)。预测触摸不仅为无触摸交互提供了一种直观的方法(即不需要与显示器进行物理接触),而且可以显著提高车辆或任何移动平台中交互式显示器的可用性,减少它们所需的注意力,提高输入精度,包括在由于道路和驾驶条件引起的扰动的影响下。这已在各种道路试验中得到证明。这种无接触互动技术可以通过最大限度地降低病原体通过触摸表面传播的风险,在新冠肺炎后的世界中得到广泛应用,例如,在公共场所、信息亭或工作场所使用售票机或自助收银机、控制面板和交互式显示器等。它还提供了一种与新兴的没有物理表面的显示技术(如2D/3D投影和虚拟或增强现实)轻松交互的方式,并提供了额外的设计灵活性,以支持包容性的设计实践。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
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