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Augmented Informative Cooperative Perception
arXiv - CS - Multimedia Pub Date : 2021-01-14 , DOI: arxiv-2101.05508 Pengyuan Zhou, Pranvera Kortoci, Yui-Pan Yau, Tristan Braud, Xiujun Wang, Benjamin Finley, Lik-Hang Lee, Sasu Tarkoma, Jussi Kangasharju, Pan Hui
arXiv - CS - Multimedia Pub Date : 2021-01-14 , DOI: arxiv-2101.05508 Pengyuan Zhou, Pranvera Kortoci, Yui-Pan Yau, Tristan Braud, Xiujun Wang, Benjamin Finley, Lik-Hang Lee, Sasu Tarkoma, Jussi Kangasharju, Pan Hui
Connected vehicles, whether equipped with advanced driver-assistance systems
or fully autonomous, are currently constrained to visual information in their
lines-of-sight. A cooperative perception system among vehicles increases their
situational awareness by extending their perception ranges. Existing solutions
imply significant network and computation load, as well as high flow of
not-always-relevant data received by vehicles. To address such issues, and thus
account for the inherently diverse informativeness of the data, we present
Augmented Informative Cooperative Perception (AICP) as the first fast-filtering
system which optimizes the informativeness of shared data at vehicles. AICP
displays the filtered data to the drivers in augmented reality head-up display.
To this end, an informativeness maximization problem is presented for vehicles
to select a subset of data to display to their drivers. Specifically, we
propose (i) a dedicated system design with custom data structure and
light-weight routing protocol for convenient data encapsulation, fast
interpretation and transmission, and (ii) a comprehensive problem formulation
and efficient fitness-based sorting algorithm to select the most valuable data
to display at the application layer. We implement a proof-of-concept prototype
of AICP with a bandwidth-hungry, latency-constrained real-life augmented
reality application. The prototype realizes the informative-optimized
cooperative perception with only 12.6 milliseconds additional latency. Next, we
test the networking performance of AICP at scale and show that AICP effectively
filter out less relevant packets and decreases the channel busy time.
中文翻译:
增强的信息合作感
目前,无论是配备先进驾驶员辅助系统还是完全自动驾驶的互联汽车,都只能在其视线范围内获得视觉信息。车辆之间的协作感知系统通过扩展感知范围来增强其态势感知。现有的解决方案意味着巨大的网络和计算负担,以及车辆接收到的并非总是相关数据的高流量。为了解决此类问题,从而说明数据固有的多样性,我们提出了增强型信息合作感知(AICP)作为第一个快速过滤系统,该系统可优化车辆共享数据的信息性。AICP以增强现实平视显示器将过滤后的数据显示给驾驶员。为此,提出了一个信息最大化问题,供车辆选择要显示给驾驶员的数据子集。具体来说,我们提出(i)具有自定义数据结构和轻量级路由协议的专用系统设计,以便于数据封装,快速解释和传输,以及(ii)全面的问题表述和基于适应度的高效排序算法,以选择最多的有价值的数据显示在应用程序层。我们使用带宽渴求,延迟受限的现实生活增强现实应用程序来实现AICP的概念验证原型。该原型仅需12.6毫秒的额外延迟即可实现信息优化的协作感知。下一个,
更新日期:2021-01-15
中文翻译:
增强的信息合作感
目前,无论是配备先进驾驶员辅助系统还是完全自动驾驶的互联汽车,都只能在其视线范围内获得视觉信息。车辆之间的协作感知系统通过扩展感知范围来增强其态势感知。现有的解决方案意味着巨大的网络和计算负担,以及车辆接收到的并非总是相关数据的高流量。为了解决此类问题,从而说明数据固有的多样性,我们提出了增强型信息合作感知(AICP)作为第一个快速过滤系统,该系统可优化车辆共享数据的信息性。AICP以增强现实平视显示器将过滤后的数据显示给驾驶员。为此,提出了一个信息最大化问题,供车辆选择要显示给驾驶员的数据子集。具体来说,我们提出(i)具有自定义数据结构和轻量级路由协议的专用系统设计,以便于数据封装,快速解释和传输,以及(ii)全面的问题表述和基于适应度的高效排序算法,以选择最多的有价值的数据显示在应用程序层。我们使用带宽渴求,延迟受限的现实生活增强现实应用程序来实现AICP的概念验证原型。该原型仅需12.6毫秒的额外延迟即可实现信息优化的协作感知。下一个,