Cooperative perception (CP) is attracting increasing attention and is regarded as the core foundation to support cooperative driving automation, a potential key solution to addressing the safety, mobility, and sustainability issues of contemporary transportation systems. However, current research on CP is still at the beginning stages where a systematic problem formulation of CP is still missing, acting as the essential guideline of the system design of a CP system under real-world situations. In this paper, we formulate a universal CP system into an optimization problem and a mobile-edge-cloud framework called Cooperverse. This system addresses CP in a mixed connectivity and automation environment. A Dynamic Feature Sharing (DFS) methodology is introduced to support this CP system under certain constraints and a Random Priority Filtering (RPF) method is proposed to conduct DFS with high performance. Experiments have been conducted based on a high-fidelity CP platform, and the results show that the Cooperverse framework is effective for dynamic node engagement and the proposed DFS methodology can improve system CP performance by 14.5% and the RPF method can reduce the communication cost for mobile nodes by 90% with only 1.7% drop for average precision.
翻译:合作观念(CP)正在引起越来越多的关注,并被视为支持合作驱动自动化的核心基础,这是解决当代运输系统安全、流动和可持续性问题的潜在关键解决办法;然而,目前对CP的研究仍处于初级阶段,仍然缺乏系统化的CP问题拟订工作,作为在现实世界形势下CP系统系统设计的基本准则;在本文件中,我们将普遍CP系统发展成一个优化问题,并建立一个称为Cooperverse的移动-前沿-边缘框架;该系统在混合连通和自动化环境中处理CP问题;采用动态特征共享(DFS)方法,在某些限制下支持这一CP系统;建议采用随机优先过滤(RPF)方法,以高性能进行外勤部工作;在高性能CP平台的基础上进行了实验,结果显示,Coovers框架对动态节点参与有效,拟议的外勤部方法可以提高14.5%的系统CP性能,而RPF方法可以将移动节点的通信费用减少90%,平均精确率下降1.7%。