  Prior works have analyzed the performance of millimeter wave automotive
radars in the presence of diverse clutter and interference scenarios using
stochastic geometry tools instead of more time-consuming measurement studies or
system-level simulations. In these works, the distributions of radars or
discrete clutter scatterers were modeled as Poisson point processes in the
Euclidean space. However, since most automotive radars are likely to be mounted
on vehicles and road infrastructure, road geometries are an important factor
that must be considered. Instead of considering each road geometry as an
individual case for study, in this work, we model each case as a specific
instance of an underlying Poisson line process and further model the
distribution of vehicles on the road as a Poisson point process - forming a
Poisson line Cox process. Then, through the use of stochastic geometry tools,
we estimate the average number of interfering radars for specific road and
vehicular densities and the effect of radar parameters such as noise and
beamwidth on the radar detection metrics. The numerical results are validated
with Monte Carlo simulations.
