Detection and segmentation of moving obstacles, along with prediction of the future occupancy states of the local environment, are essential for autonomous vehicles to proactively make safe and informed decisions. In this paper, we propose a framework that integrates the two capabilities together using deep neural network architectures. Our method first detects and segments moving objects in the scene, and uses this information to predict the spatiotemporal evolution of the environment around autonomous vehicles. To address the problem of direct integration of both static-dynamic object segmentation and environment prediction models, we propose using occupancy-based environment representations across the whole framework. Our method is validated on the real-world Waymo Open Dataset and demonstrates higher prediction accuracy than baseline methods.

翻译：对移动障碍的探测和分解,以及预测今后当地环境的占用状态,对于自主车辆积极主动地作出安全和知情的决定至关重要。在本文件中,我们提出一个框架,利用深神经网络结构将这两种能力结合起来。我们的方法首先探测和分块移动现场物体,并利用这一信息预测自主车辆周围环境的瞬间演变。为了解决静态动力物体分解和环境预测模型直接结合的问题,我们提议在整个框架内使用基于占用的环境表示。我们的方法在现实世界Waymo公开数据集中验证,并显示比基线方法更高的预测准确性。