Distributed Complementary Fusion for Connected Vehicles

James Klupacs, A. Gostar, A. Bab-Hadiashar, Jennifer Palmer, R. Hoseinnezhad
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Abstract

We present a random finite set-based method for achieving comprehensive situation awareness by each vehicle in a distributed vehicle network. Our solution is designed for labeled multi-Bernoulli filters running in each vehicle. It involves complementary fusion of sensor information locally running through consensus iterations. We introduce a novel label merging algorithm to eliminate double counting. We also extend the label space to incorporate sensor identities. This helps to overcome label inconsistencies. We show that the proposed algorithm is able to outperform the standard LMB filter using a distributed complementary approach with limited fields of view.
面向互联汽车的分布式互补融合
我们提出了一种基于随机有限集的方法来实现分布式车辆网络中每辆车辆的综合态势感知。我们的解决方案是为在每辆车上运行的标记多伯努利滤波器而设计的。它涉及通过共识迭代在局部运行的传感器信息的互补融合。提出了一种新的标签合并算法来消除重复计数。我们还扩展了标签空间以包含传感器身份。这有助于克服标签的不一致性。我们表明,该算法能够优于使用有限视场的分布式互补方法的标准LMB滤波器。
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