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.

通过将场景表示为动态占据栅格图（dynamic occupancy grid maps，DOGMs），将语义标签与占用的栅格单元关联并结合地图信息，我们提出了一个结合深度学习的时空和概率方法的新型框架来预测车辆行为，并通过与真实注释进行评估，在真实世界的 NuScenes 数据集上验证了我们的结果，表明我们的模型相对于传统的 OGM 预测方法，能够更准确地预测静态和动态车辆的行为，此外，我们进行了消融研究，并评估了语义标签和地图在架构中的作用。

利用先验信息和语义辅助占据栅格地图进行车辆运动预测

Vehicle Motion Forecasting using Prior Information and Semantic-assisted  Occupancy Grid Maps

In this work, we tackle the problem of modeling the vehicle environment as
dynamic occupancy grid map in complex urban scenarios using recurrent neural
networks. Dynamic occupancy grid maps represent the scene in a bird's eye view,
where each grid cell contains the occupancy probability and the two dimensional
velocity. As input data, our approach relies on measurement grid maps, which
contain occupancy probabilities, generated with lidar measurements. Given this
configuration, we propose a recurrent neural network architecture to predict a
dynamic occupancy grid map, i.e. filtered occupancy and velocity of each cell,
by using a sequence of measurement grid maps. Our network architecture contains
convolutional long-short term memories in order to sequentially process the
input, makes use of spatial context, and captures motion. In the evaluation, we
quantify improvements in estimating the velocity of braking and turning
vehicles compared to the state-of-the-art. Additionally, we demonstrate that
our approach provides more consistent velocity estimates for dynamic objects,
as well as, less erroneous velocity estimates in static area.

使用递归神经网络模拟车辆环境为动态格网图，从激光雷达测量的占用概率图中估计转向和刹车车辆的速度，提供更加一致和准确的速度估计。