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：为使自动驾驶汽车能够主动规划安全轨迹并做出明智决策，必须能够预测局部环境未来的占据状态。然而，占据预测的常见问题包括移动物体在预测中消失或变得模糊，尤其在较长时间跨度上。我们提出了一种利用环境语义进行未来占据预测的环境预测框架。该方法首先对环境进行语义分割，并利用该信息与占据信息共同预测环境的时空演化。我们在真实世界的Waymo开放数据集上验证了我们的方法。与基线方法相比，我们的模型具有更高的预测精度，并且能够在更长的预测时间跨度内保持移动物体的外观特征。