For an autonomous vehicle it is essential to observe the ongoing dynamics of a scene and consequently predict imminent future scenarios to ensure safety to itself and others. This can be done using different sensors and modalities. In this paper we investigate the usage of optical flow for predicting future semantic segmentations. To do so we propose a model that forecasts flow fields autoregressively. Such predictions are then used to guide the inference of a learned warping function that moves instance segmentations on to future frames. Results on the Cityscapes dataset demonstrate the effectiveness of optical-flow methods.

翻译：对于自动驾驶车辆而言，观察场景的持续动态并据此预测即将发生的未来场景，对于确保自身及他人安全至关重要。这可通过不同传感器与模态实现。本文研究利用光流预测未来语义分割的方法。为此，我们提出一种自回归预测光流场的模型。此类预测随后用于引导学习扭曲函数的推断，该函数可将实例分割迁移至未来帧。在Cityscapes数据集上的结果展示了光流方法的有效性。