  Motion prediction is a challenging task for autonomous vehicles due to
uncertainty in the sensor data, the non-deterministic nature of future, and
complex behavior of agents. In this paper, we tackle this problem by
representing the scene as dynamic occupancy grid maps (DOGMs), associating
semantic labels to the occupied cells and incorporating map information. We
propose a novel framework that combines deep-learning-based spatio-temporal and
probabilistic approaches to predict vehicle behaviors.Contrary to the
conventional OGM prediction methods, evaluation of our work is conducted
against the ground truth annotations. We experiment and validate our results on
real-world NuScenes dataset and show that our model shows superior ability to
predict both static and dynamic vehicles compared to OGM predictions.
Furthermore, we perform an ablation study and assess the role of semantic
labels and map in the architecture.
