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.

翻译：运动预测是自动驾驶领域的一项挑战性任务，这源于传感器数据的不确定性、未来状态的不可确定性以及交通参与者行为的复杂性。本文通过将场景表示为动态占据栅格图（DOGMs），为占据单元格关联语义标签并融合地图信息来解决该问题。我们提出了一种结合基于深度学习的时空建模与概率方法的新型框架，用于预测车辆行为。与传统的OGM预测方法不同，本研究采用真实标注进行算法评估。我们在真实场景的NuScenes数据集上开展实验与验证，结果表明相较于OGM预测方法，本模型在静态与动态车辆预测方面均展现出更优性能。此外，我们通过消融实验评估了语义标签与地图信息在架构中的作用。