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.

翻译：在这项工作中,我们用经常的神经网络来解决在复杂的城市情景中将车辆环境建模为动态占用网格图的问题。动态占用网格图在鸟眼视图中代表着场景,每个网格网格图包含占用概率和两维速度。作为输入数据,我们的方法依赖于测量网格图,其中含有使用利达尔测量产生的占用概率。根据这种配置,我们提议一个经常性的神经网络结构,通过使用测量网格图的顺序,预测动态占用网格图,即每个细胞的过滤占用和速度。我们的网络结构包含动态长短期记忆,以便按顺序处理输入,利用空间环境,并捕捉移动。在评估中,我们量化了与最新技术相比,在估计制动速度和旋转车辆方面作出的改进。此外,我们证明我们的方法为动态物体提供了更加一致的速度估计,以及静态区域较不错误的速度估计。