Predicting the future occupancy state of an environment is important to enable informed decisions for autonomous vehicles. Common challenges in occupancy prediction include vanishing dynamic objects and blurred predictions, especially for long prediction horizons. In this work, we propose a double-prong neural network architecture to predict the spatiotemporal evolution of the occupancy state. One prong is dedicated to predicting how the static environment will be observed by the moving ego vehicle. The other prong predicts how the dynamic objects in the environment will move. Experiments conducted on the real-world Waymo Open Dataset indicate that the fused output of the two prongs is capable of retaining dynamic objects and reducing blurriness in the predictions for longer time horizons than baseline models.

翻译：预测一个环境的未来占用状态对于为自主车辆做出知情决定非常重要。 使用预测方面的共同挑战包括消失动态物体和模糊预测,特别是对长期预测前景的模糊预测。 在这项工作中,我们提议一个双进程神经网络结构,以预测占用状态的瞬时演变。 一个要点是预测移动自我飞行器将如何观测静态环境。另一个要点预测环境中动态物体将如何移动。 在真实世界Waymo开放数据集上进行的实验显示,两个分数的导出电能够保留动态物体,并减少预测中比基线模型更长的时间范围模糊。