Visibility and Interactive Information Sharing in Collaborative Sensing Systems

协作传感系统中的可见性和交互信息共享自动驾驶车辆和移动机器人具有带来变革性的技术和社会变革的潜力。为了自主决策，节点需要对动态环境中的实体进行识别和跟踪，从而获得合理程度的态势感知。这在部分遮挡的环境中可能是不可能的，其中单个节点可能具有有限的可见性，除非节点参与协作感测，即共享感测信息。与云中或网络边缘的集中资源共享原始/处理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。这就需要研究分布式协作传感框架，利用强大的跟踪算法和深度学习模型实现可靠的识别/分类任务。特别感兴趣的是协作传感器在被遮挡的环境中可以“看到”什么，以及人们应该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，即它们的态势感知。这项拟议的研究工作将推进协作传感系统的最新发展，通过在教育创新、实现多样性、让社区和行业参与以及向更广泛的公众传播成果方面的有计划的努力，有望使该领域和社会更广泛地受益。该提议的核心是研究在障碍/动态环境中的协作传感，例如可能被用于实现自动驾驶车辆和自动机器人。中心挑战是在可能的通信和/或计算受限的环境中，基于分布式感知资源实现前所未有的实时态势感知。这项拟议的研究整合了三个研究重点。第一个是对随机环境中的分布式传感单元集合可见的基本理解的进步。这项工作将利用随机几何模型和分析，为典型随机环境的“可见性”提供可靠的定量绩效评估。在这项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。第二个重点是发展分布式协作感知的基本基础，重点是优化交互信息共享和/或适应不断变化的环境背景，以便在自治但协作的节点之间共同最大限度地提高态势感知。我们将提供由优化问题的结构特性(例如子模块)和交互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。第三个推动力是开发一个按比例缩小的平台，用于替代协作传感系统设计的受控和可重复试验。最后一个推力不仅是为了提供促进研究的平台，而且是一项吸引大量本科生的活动，也是我们教育努力的跳板。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.