Visibility and Interactive Information Sharing in Collaborative Sensing Systems

协作传感系统中的可视性和交互式信息共享自动驾驶车辆和移动的机器人有可能带来变革性的技术和社会变革。为了做出自主决策，节点需要通过识别和跟踪动态环境中的实体来实现合理程度的态势感知。 这在局部遮挡的环境中可能是不可能的，其中各个节点可能具有有限的可见性，除非节点参与协作感测，即，分享感知信息与云中或网络边缘处的集中式资源共享原始/经处理的实时感测数据造成潜在的高通信/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作传感框架的需求，这些框架利用强大的跟踪算法和深度学习模型进行可靠的识别/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"什么的表征，以及在资源受限的设置中应该如何实现信息共享以公平地优化节点"知道"什么，即，他们的情景意识。拟议的研究工作将推进最先进的协作传感系统，预计将有利于该领域和社会更广泛地，通过计划的努力，在教育创新，实现多样性，参与社区和行业，并传播成果给更广泛的公众。例如可以用于实现自动驾驶车辆和自动机器人。中心的挑战是实现一个前所未有的水平的实时态势感知的基础上分布式传感资源在可能的通信和/或计算约束的设置。本研究整合了三个研究方向。第一个是进步的基本理解是可见的分布式传感单元在随机环境中的集合。这项工作将利用随机几何模型和分析，对典型随机环境的"可见性"进行可靠的定量性能评估。在这个研究的推力确定的性能限制将告知什么分布式系统可以“知道”在资源受限的设置。第二个重点是分布式协作传感的基本基础的发展，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，以共同最大限度地提高自主但协作节点之间的态势感知。我们将提供由优化问题的结构特性驱动的新方法（例如，子模块化）和交互式信息共享协议，以促进分布式对象识别和跟踪。第三个推力是开发一个缩小规模的平台，用于替代协作传感系统设计的受控和可重复实验。最后一个推力不仅是为了提供平台，以推进研究，但也是一个活动，从事大量的本科生和跳板，我们的教育工作。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的智力价值和更广泛的影响审查标准。

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.