Mutual interference among the radar sensors has become a serious concern due to the extensive growth of self driving vehicles (SDV) equipped with such sensors. The problem becomes more severe with the increase in traffic density, where a large number of SDVs gather within the proximity of inter-vehicular radars. Thereby, the number of resources required to maintain the neighbourhood orthogonality increases with traffic density and further leads to the problem of radar blindness. In this paper, we propose a graph-based resource allocation (GRA) scheme to assign resources to the running SDV pool. GRA assures that two closely located SDVs may not simultaneously use the same resource. Also, we integrate the notion of traffic-based dynamic-range approach (TDA) with GRA. Then, through simulation results, it is shown that GRA outperforms the state of art random allocation approach. Further, it is shown that GRA, along with TDA, may eliminate the problem of radar blindness.

中文翻译：

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基于雷达的自动驾驶车辆的盲取消

由于配备此类传感器的自动驾驶汽车 (SDV) 的广泛增长，雷达传感器之间的相互干扰已成为一个严重问题。随着交通密度的增加，问题变得更加严重，大量的 SDV 聚集在车载雷达附近。因此，维持邻域正交性所需的资源数量随着交通密度的增加而增加，并进一步导致雷达盲的问题。在本文中，我们提出了一种基于图的资源分配 (GRA) 方案，以将资源分配给正在运行的 SDV 池。GRA 确保两个靠近的 SDV 不会同时使用相同的资源。此外，我们将基于流量的动态范围方法 (TDA) 的概念与 GRA 相结合。然后通过仿真结果，结果表明，GRA 优于最先进的随机分配方法。此外，还表明 GRA 与 TDA 一起可以消除雷达盲区的问题。