  Grid maps are widely established for the representation of static objects in
robotics and automotive applications. Though, incorporating velocity
information is still widely examined because of the increased complexity of
dynamic grids concerning both velocity measurement models for radar sensors and
the representation of velocity in a grid framework. In this paper, both issues
are addressed: sensor models and an efficient grid framework, which are
required to ensure efficient and robust environment perception with radar. To
that, we introduce new inverse radar sensor models covering radar sensor
artifacts such as measurement ambiguities to integrate automotive radar sensors
for improved velocity estimation.
  Furthermore, we introduce UNIFY, a multiple belief Bayesian grid map
framework for static occupancy and velocity estimation with independent layers.
The proposed UNIFY framework utilizes a grid-cell-based layer to provide
occupancy information and a particle-based velocity layer for motion state
estimation in an autonomous vehicle's environment. Each UNIFY layer allows
individual execution as well as simultaneous execution of both layers for
optimal adaption to varying environments in autonomous driving applications.
  UNIFY was tested and evaluated in terms of plausibility and efficiency on a
large real-world radar data-set in challenging traffic scenarios covering
different densities in urban and rural sceneries.
