  Through constant improvements in recent years radar sensors have become a
viable alternative to lidar as the main distancing sensor of an autonomous
vehicle. Although robust and with the possibility to directly measure the
radial velocity, it brings it's own set of challenges, for which existing
algorithms need to be adapted. One core algorithm of a perception system is
dynamic occupancy grid mapping, which has traditionally relied on lidar. In
this paper we present a dual-weight particle filter as an extension for a
Bayesian occupancy grid mapping framework to allow to operate it with radar as
its main sensors. It uses two separate particle weights that are computed
differently to compensate that a radial velocity measurement in many situations
is not able to capture the actual velocity of an object. We evaluate the method
extensively with simulated data and show the advantages over existing single
weight solutions.
