  Tremendous progress in deep learning over the last years has led towards a
future with autonomous vehicles on our roads. Nevertheless, the performance of
their perception systems is strongly dependent on the quality of the utilized
training data. As these usually only cover a fraction of all object classes an
autonomous driving system will face, such systems struggle with handling the
unexpected. In order to safely operate on public roads, the identification of
objects from unknown classes remains a crucial task. In this paper, we propose
a novel pipeline to detect unknown objects. Instead of focusing on a single
sensor modality, we make use of lidar and camera data by combining state-of-the
art detection models in a sequential manner. We evaluate our approach on the
Waymo Open Perception Dataset and point out current research gaps in anomaly
detection.
