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Prior works have analyzed the performance of millimeter wave automotiveradars in the presence of diverse clutter and interference scenarios usingstochastic geometry tools instead of more time-consuming measurement studies orsystem-level simulations. In these works, the distributions of radars ordiscrete clutter scatterers were modeled as Poisson point processes in theEuclidean space. However, since most automotive radars are likely to be mountedon vehicles and road infrastructure, road geometries are an important factorthat must be considered. Instead of considering each road geometry as anindividual case for study, in this work, we model each case as a specificinstance of an underlying Poisson line process and further model thedistribution of vehicles on the road as a Poisson point process - forming aPoisson line Cox process. Then, through the use of stochastic geometry tools,we estimate the average number of interfering radars for specific road andvehicular densities and the effect of radar parameters such as noise andbeamwidth on the radar detection metrics. The numerical results are validatedwith Monte Carlo simulations.

Impact of Urban Street Geometry on the Detection Probability of Automotive Radars