At present, 77GHz millimeter-wave (MMW) radar has become a critical sensor in
intelligent transportation systems due to its all-weather detection capability,
which enables it to resist complex weather and light interference. Radar cross
section (RCS) is a significant characteristic of radar, greatly impacting the
detection quality of traffic targets across various traffic scenarios. RCS is
usually measured in an anechoic chamber to establish a model of the RCS of
typical traffic participants. However, due to large target fluctuations and
multi-angle scattering centers of targets, representing the RCS characteristics
of typical traffic participants with a single point is challenging. Taking
global vehicle target (GVT), pedestrian target and cyclist target as examples,
this paper proposes a method for measuring and modeling the RCS features of
typical traffic participants. For the static RCS features of targets, we
measured the RCS of the target under different viewing angles in an anechoic
chamber, establishing an RCS feature analysis method for the target based on the
radar viewing angle. For the dynamic RCS features of targets, we collected radar
reflection data of typical traffic participants from different perspectives
while in motion in an open-field environment. We then extracted and synthesized
the strong scattering centers of the target, established a dynamic RCS feature
model, and fitted the RCS boundary curve from the near field to the far field.
Based on the road scenario, we established the RCS simulation scenario to
validate the effectiveness of the target dynamic RCS simulation model in the
road scenario. The results of testing and analysis show that radar enables
stable characterization and modeling of target RCS, thereby enhancing the
environmental perception capabilities of autonomous vehicles and improving
driving safety within intelligent transportation systems.