Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps
This work addresses safety-critical object detection for automated vehicles, but it is incremental as it adapts an existing detector to a new domain.
The paper tackles the problem of detecting generic dynamic objects for automated driving by using a LiDAR-based dynamic grid map and a deep learning detector, resulting in a strong reduction of false positive object detection rates.
This paper describes a method to detect generic dynamic objects for automated driving. First, a LiDAR-based dynamic grid is generated online. Second, a deep learning-based detector is trained on the dynamic grid to infer the presence of dynamic objects of any type, which is a prerequisite for safe automated vehicles in arbitrary, edge-case scenarios. The Rotation-equivariant Detector (ReDet) - originally designed for oriented object detection on aerial images - was chosen due to its high detection performance. Experiments are conducted based on real sensor data and the benefits in comparison to classic dynamic cell clustering strategies are highlighted. The false positive object detection rate is strongly reduced by the proposed approach.