For autonomous vehicles to proactively plan safe trajectories and make informed decisions, they must be able to predict the future occupancy states of the local environment. However, common issues with occupancy prediction include predictions where moving objects vanish or become blurred, particularly at longer time horizons. We propose an environment prediction framework that incorporates environment semantics for future occupancy prediction. Our method first semantically segments the environment and uses this information along with the occupancy information to predict the spatiotemporal evolution of the environment. We validate our approach on the real-world Waymo Open Dataset. Compared to baseline methods, our model has higher prediction accuracy and is capable of maintaining moving object appearances in the predictions for longer prediction time horizons.

翻译：为使自动驾驶车辆能够主动规划安全轨迹并做出明智决策，必须预测局部环境的未来占据状态。然而，占据预测的常见问题在于预测中运动物体会消失或模糊，尤其当预测时间跨度较长时。我们提出一种融合环境语义信息的环境预测框架，用于未来占据状态预测。该方法首先对场景进行语义分割，随后利用语义信息与占据信息共同预测环境的时空演变。我们在真实世界的Waymo开放数据集上验证了该方法。与基线方法相比，本模型具有更高的预测精度，并能在更长的预测时间跨度内保持预测中运动物体的外观特征。