The impact of discrete clutter and co-channel interference on the performance of automotive radar networks has been studied using stochastic geometry, in particular, by leveraging two-dimensional Poisson point processes (PPPs). However, such characterization does not take into account the impact of street geometry and the fact that the location of the automotive radars are restricted to the streets as their domain rather than the entire Euclidean plane. In addition, the structure of the streets may change drastically as a vehicle moves out of a city center towards the outskirts. Consequently, not only the radar performance change but also the radar parameters and protocols must be adapted for optimum performance. In this paper, we propose and characterize line and Cox process-based street and point models to analyze large-scale automotive radar networks. We consider the classical Poisson line process (PLP) and the newly introduced Binomial line process (BLP) model to emulate the streets and the corresponding PPP-based Cox process to emulate the vehicular nodes. In particular, the BLP model effectively considers the spatial variation of street geometry across different parts of the city. We derive the effective interference set experienced by an automotive radar, the statistics of distance to interferers, and characterize the detection probability of the ego radar as a function of street and vehicle density. Finally, leveraging the real-world data on urban streets and vehicle density across different cities of the world, we present how the radar performance varies in different parts of the city as well as across different times of the day. Thus, our study equips network operators and automotive manufacturers with essential system design insights to plan and optimize automotive radar networks.

翻译：已有研究利用随机几何，特别是借助二维泊松点过程（PPP），分析了离散杂波和同信道干扰对车载雷达网络性能的影响。然而，此类表征未考虑街道几何结构的影响，以及车载雷达的位置受限于街道区域而非整个欧几里得平面这一事实。此外，当车辆从市中心驶向郊区时，街道结构可能发生剧烈变化。因此，不仅雷达性能会改变，雷达参数和协议也必须进行调整以实现最佳性能。本文提出并表征了基于线和Cox过程的街道与节点模型，以分析大规模车载雷达网络。我们采用经典的泊松线过程（PLP）和新引入的二项式线过程（BLP）模型来模拟街道，并使用相应的基于PPP的Cox过程来模拟车辆节点。特别地，BLP模型有效考虑了城市不同区域街道几何结构的空间变化。我们推导了车载雷达所经历的有效干扰集、干扰源距离的统计特性，并将自车雷达的检测概率表征为街道密度和车辆密度的函数。最后，利用全球不同城市中真实世界的街道和车辆密度数据，我们展示了雷达性能在城市不同区域以及一天中不同时段的变化情况。因此，本研究为网络运营商和汽车制造商提供了重要的系统设计见解，以规划和优化车载雷达网络。