The ability to predict future structure features of environments based on past perception information is extremely needed by autonomous vehicles, which helps to make the following decision-making and path planning more reasonable. Recently, point cloud prediction (PCP) is utilized to predict and describe future environmental structures by the point cloud form. In this letter, we propose a novel efficient Transformer-based network to predict the future LiDAR point clouds exploiting the past point cloud sequences. We also design a semantic auxiliary training strategy to make the predicted LiDAR point cloud sequence semantically similar to the ground truth and thus improves the significance of the deployment for more tasks in real-vehicle applications. Our approach is completely self-supervised, which means it does not require any manual labeling and has a solid generalization ability toward different environments. The experimental results show that our method outperforms the state-of-the-art PCP methods on the prediction results and semantic similarity, and has a good real-time performance. Our open-source code and pre-trained models are available at https://github.com/Blurryface0814/PCPNet.

翻译：基于历史感知信息预测环境未来结构特征的能力对于自动驾驶车辆至关重要，这有助于使后续决策制定和路径规划更加合理。近年来，点云预测（PCP）被用于通过点云形式预测并描述未来环境结构。本文提出了一种新颖的高效Transformer网络，利用历史点云序列预测未来激光雷达点云。我们还设计了一种语义辅助训练策略，使预测的激光雷达点云序列在语义上与真实值相似，从而提升了该方法在实车应用中部署于更多任务的意义。我们的方法完全基于自监督学习，这意味着它无需任何人工标注，并对不同环境具有良好的泛化能力。实验结果表明，我们的方法在预测结果和语义相似性方面优于最先进的PCP方法，并具有良好的实时性能。我们的开源代码和预训练模型可在https://github.com/Blurryface0814/PCPNet获取。