Paper ID: 2410.14799 • Published Oct 18, 2024
Deep Generic Dynamic Object Detection Based on Dynamic Grid Maps
Rujiao Yan, Linda Schubert, Alexander Kamm, Matthias Komar, Matthias Schreier
TL;DR
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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.