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Visibility and Interactive Information Sharing in Collaborative Sensing Systems

协作传感系统中的可见性和交互式信息共享

基本信息

批准号：
1809327
负责人：
Gustavo de Veciana
金额：
$ 45万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1809327&HistoricalAwards=false
关键词：
Visibility Interactive Information Sharing Collaborative

项目摘要

Visibility and Interactive Information Sharing in Collaborative Sensing SystemsSelf-driving vehicles and mobile robots have the potential to deliver transformative technological and societal changes. In order to make autonomous decisions, nodes need to have a reasonable degree of situational awareness achieved through recognition and tracking of entities in dynamic environments. This may not be possible in partially occluded environments, where individual nodes may have limited visibility, unless nodes participate in collaborative sensing, i.e., share sensed information. Sharing raw/processed real-time sensing data with centralized resources in the cloud or at the network edge poses potentially high communication/computational burdens, particular in safety critical settings requiring low latency. This motivates the need to study distributed collaborative sensing frameworks leveraging powerful algorithms for tracking and deep learning models for reliable recognition/classification tasks. Of particular interest is a characterization of what collaborating sensors can ``see'' in occluded environments and how one should realize information sharing in resource constrained settings to fairly optimize what nodes ``know'', i.e., their situational awareness. The proposed research effort will advance the state-of-the-art in collaborative sensing systems which are expected to benefit the field and society more broadly, through planned efforts in education innovation, achieving diversity, engaging the community and industry, and disseminating results to a wider public.This proposal centers on the study of collaborative sensing in obstructed/dynamic environments, such as might be used to enable self-driving vehicles and autonomous robots. The central challenge is to achieve an unprecedented level of real-time situational awareness based on distributed sensing resources in a possibly communication and/or computationally constrained setting. The proposed research integrates three research thrusts. The first is the advancement of the fundamental understanding what is visible to sets of distributed sensing units in stochastic environments. This work will leverage stochastic geometric models and analysis to provide robust quantitative performance assessment of `visibility' for typical random environments. The performance limits determined in this research thrust will inform what a distributed system can ``know" in resource constrained settings. The second thrust is the development of fundamental underpinnings of distributed collaborative sensing with a focus on the optimization of interactive information sharing and/or adaptation to changing environmental contexts so as to jointly maximize situational awareness amongst autonomous yet collaborating nodes. We will provide new approaches driven by structural properties of the optimization problems (e.g., submodularity) and interactive information sharing protocols to facilitate distributed object recognition and tracking. The third thrust is the development of a scaled-down platform for controlled and reproducible experimentation of alternative collaborative sensing system designs. The last thrust is not only geared at providing platform to advance the research but is also an activity to engage a substantial number of undergraduates and a springboard to our educational efforts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

协作传感系统中的可见性和交互式信息共享自动驾驶汽车和移动机器人具有带来变革性技术和社会变革的潜力。为了进行自主决策，节点需要通过对动态环境中实体的识别和跟踪来实现合理程度的态势感知。这在部分闭塞的环境中可能是不可能的，因为单个节点的可见性可能有限，除非节点参与协作感知，即共享感知到的信息。与云端或网络边缘的集中资源共享原始/处理过的实时传感数据可能会带来很高的通信/计算负担，特别是在需要低延迟的安全关键设置中。这激发了研究分布式协同传感框架的需求，利用强大的算法来跟踪和深度学习模型，以实现可靠的识别/分类任务。特别感兴趣的是协作传感器在闭塞环境中可以“看到”的特征，以及如何在资源受限的环境中实现信息共享，以公平地优化节点“知道”的内容，即他们的态势感知。拟议的研究工作将推进最先进的协作传感系统，预计将通过计划中的教育创新、实现多样性、参与社区和工业以及向更广泛的公众传播结果，更广泛地造福于该领域和社会。该提案以研究障碍物/动态环境中的协同传感为中心，例如可能用于实现自动驾驶汽车和自主机器人。核心挑战是在可能的通信和/或计算受限的环境中，基于分布式传感资源实现前所未有的实时态势感知水平。拟议的研究整合了三个研究重点。首先是对随机环境中分布式传感单元集合可见性的基本理解的进步。这项工作将利用随机几何模型和分析，为典型的随机环境提供强大的“可见性”定量性能评估。在本研究中确定的性能限制将告知分布式系统在资源受限的情况下可以“知道”什么。第二个重点是分布式协同传感的基础基础的发展，重点是优化交互式信息共享和/或适应不断变化的环境背景，以便在自主但协作的节点之间共同最大化态势感知。我们将提供由优化问题的结构属性（例如，子模块化）和交互式信息共享协议驱动的新方法，以促进分布式目标识别和跟踪。第三个重点是开发一个按比例缩小的平台，用于替代协同传感系统设计的可控和可重复实验。最后一个推动力不仅是为推进研究提供平台，也是一项吸引大量本科生的活动，也是我们教育工作的跳板。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。