  We present a LiDAR-based and real-time capable 3D perception system for
automated driving in urban domains. The hierarchical system design is able to
model stationary and movable parts of the environment simultaneously and under
real-time conditions. Our approach extends the state of the art by innovative
in-detail enhancements for perceiving road users and drivable corridors even in
case of non-flat ground surfaces and overhanging or protruding elements. We
describe a runtime-efficient pointcloud processing pipeline, consisting of
adaptive ground surface estimation, 3D clustering and motion classification
stages. Based on the pipeline's output, the stationary environment is
represented in a multi-feature mapping and fusion approach. Movable elements
are represented in an object tracking system capable of using multiple
reference points to account for viewpoint changes. We further enhance the
tracking system by explicit consideration of occlusion and ambiguity cases. Our
system is evaluated using a subset of the TUBS Road User Dataset. We enhance
common performance metrics by considering application-driven aspects of
real-world traffic scenarios. The perception system shows impressive results
and is able to cope with the addressed scenarios while still preserving
real-time capability.
