Predicting future trajectories of road agents is a critical task for autonomous driving. Recent goal-based trajectory prediction methods, such as DenseTNT and PECNet, have shown good performance on prediction tasks on public datasets. However, they usually require complicated goal-selection algorithms and optimization. In this work, we propose KEMP, a hierarchical end-to-end deep learning framework for trajectory prediction. At the core of our framework is keyframe-based trajectory prediction, where keyframes are representative states that trace out the general direction of the trajectory. KEMP first predicts keyframes conditioned on the road context, and then fills in intermediate states conditioned on the keyframes and the road context. Under our general framework, goal-conditioned methods are special cases in which the number of keyframes equal to one. Unlike goal-conditioned methods, our keyframe predictor is learned automatically and does not require hand-crafted goal-selection algorithms. We evaluate our model on public benchmarks and our model ranked 1st on Waymo Open Motion Dataset Leaderboard (as of September 1, 2021).

翻译：预测道路物剂的未来轨迹是自主驾驶的关键任务。最近基于目标的轨迹预测方法,如登塞图恩特和PECNet,在公共数据集的预测任务上表现良好,但通常需要复杂的客观算法和优化。在这项工作中,我们提议为轨迹预测建立一个分级的端到端深学习框架KEMP。我们框架的核心是以关键框架为基础的轨迹预测,其中关键框架具有代表性,表明轨道的总方向。KEMP首先预测道路环境的关键框架,然后以关键框架和道路背景为条件填补中间状态。在我们的一般框架内,目标设定的方法是特殊案例,其中关键框架的数目等于一个。与目标设定的方法不同,我们的关键框架预测器是自动学习的,不需要手工制作的目标选择算法。我们评估了公共基准模型和Waymo Open Datatset check 领头板上的模型(自2021年9月1日起)。