Environment prediction frameworks are integral for autonomous vehicles, enabling safe navigation in dynamic environments. LiDAR generated occupancy grid maps (L-OGMs) offer a robust bird's eye-view scene representation that facilitates joint scene predictions without relying on manual labeling unlike commonly used trajectory prediction frameworks. Prior approaches have optimized deterministic L-OGM prediction architectures directly in grid cell space. While these methods have achieved some degree of success in prediction, they occasionally grapple with unrealistic and incorrect predictions. We claim that the quality and realism of the forecasted occupancy grids can be enhanced with the use of generative models. We propose a framework that decouples occupancy prediction into: representation learning and stochastic prediction within the learned latent space. Our approach allows for conditioning the model on other available sensor modalities such as RGB-cameras and high definition maps. We demonstrate that our approach achieves state-of-the-art performance and is readily transferable between different robotic platforms on the real-world NuScenes, Waymo Open, and a custom dataset we collected on an experimental vehicle platform.

翻译：环境预测框架是自动驾驶汽车的关键组成部分，能够确保在动态环境中安全导航。激光雷达生成的占用栅格地图（L-OGM）提供了鲁棒的鸟瞰场景表示，可支持联合场景预测，且无需依赖常见轨迹预测框架所需的人工标注。现有方法直接在栅格单元空间中优化确定性L-OGM预测架构，尽管这些方法在预测任务中取得了一定成功，但偶尔会生成不真实或错误的预测结果。我们主张通过生成模型可提升预测占用栅格的质量与真实性。为此提出一种将占用预测解耦为表示学习与潜空间随机预测的框架。该方法支持利用其他传感器模态（如RGB摄像头和高精地图）对模型进行条件约束。实验表明，我们的方法在真实世界的NuScenes、Waymo Open数据集以及通过实验车辆平台自采数据集上均达到最优性能，且可便捷地迁移至不同机器人平台。