  LiDAR-based 3D object detectors have achieved unprecedented speed and
accuracy in autonomous driving applications. However, similar to other neural
networks, they are often biased toward high-confidence predictions or return
detections where no real object is present. These types of detections can lead
to a less reliable environment perception, severely affecting the functionality
and safety of autonomous vehicles. We address this problem by proposing LS-VOS,
a framework for identifying outliers in 3D object detections. Our approach
builds on the idea of Virtual Outlier Synthesis (VOS), which incorporates
outlier knowledge during training, enabling the model to learn more compact
decision boundaries. In particular, we propose a new synthesis approach that
relies on the latent space of an auto-encoder network to generate outlier
features with a parametrizable degree of similarity to in-distribution
features. In extensive experiments, we show that our approach improves the
outlier detection capabilities of a state-of-the-art object detector while
maintaining high 3D object detection performance.
