论文标题
目标导演演员的目标定向占用预测
Goal-Directed Occupancy Prediction for Lane-Following Actors
论文作者
论文摘要
预测在共享道路上行驶的车辆的未来行为是安全自动驾驶的至关重要的任务。许多现有的问题方法努力将所有可能的车辆行为提炼成简化的一组高级动作。但是，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发生的全部操作。为了消除这种缺陷，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。我们表明，我们的方法能够准确预测与映射的车道几何形状保持一致的未来占用率，并且自然地基于本地场景上下文捕获了多模式，同时也没有在先前的工作中观察到的模式崩溃问题。
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.
