Predicting the possible future behaviors of vehicles that drive on shared roads is a crucial task for safe autonomous driving. Many existing approaches to this problem strive to distill all possible vehicle behaviors into a simplified set of high-level actions. However, these action categories do not suffice to describe the full range of maneuvers possible in the complex road networks we encounter in the real world. To combat this deficiency, we propose a new method that leverages the mapped road topology to reason over possible goals and predict the future spatial occupancy of dynamic road actors. We show that our approach is able to accurately predict future occupancy that remains consistent with the mapped lane geometry and naturally captures multi-modality based on the local scene context while also not suffering from the mode collapse problem observed in prior work.

中文翻译：

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车道跟随演员的目标导向占用预测

预测在共享道路上行驶的车辆未来可能的行为是安全自动驾驶的关键任务。解决这个问题的许多现有方法都努力将所有可​​能的车辆行为提炼成一组简化的高级动作。然而，这些动作类别不足以描述我们在现实世界中遇到的复杂道路网络中可能出现的所有操作范围。为了克服这一缺陷，我们提出了一种新方法，该方法利用映射的道路拓扑来推理可能的目标并预测动态道路参与者的未来空间占用。