论文标题
从目标，航路点和路径到长期的人类轨迹预测
From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting
论文作者
论文摘要
人类轨迹预测是一个固有的多模式问题。未来轨迹的不确定性源于两个来源：（a）代理人知道但模型未知的来源，例如长期目标和（b）代理和模型未知的来源，例如其他代理商的意图和不可约束的随机性。我们建议将这种不确定性分配到其认知和态度来源中。我们通过多模式在长期目标中通过多模态建模认识的联合国性，并通过路径和路径中的多模态进行了不确定性。为了说明这种二分法，我们还提出了一种新型的长期轨迹预测设置，预测范围为一分钟，比以前的工作更长。最后，我们介绍了一个场景，这是一个场景的轨迹预测网络，它利用了跨长期预测范围内的多种轨迹预测的多元轨迹预测。 Stanford Drone＆Drone＆Intersection无人机数据集。
Human trajectory forecasting is an inherently multi-modal problem. Uncertainty in future trajectories stems from two sources: (a) sources that are known to the agent but unknown to the model, such as long term goals and (b)sources that are unknown to both the agent & the model, such as intent of other agents & irreducible randomness indecisions. We propose to factorize this uncertainty into its epistemic & aleatoric sources. We model the epistemic un-certainty through multimodality in long term goals and the aleatoric uncertainty through multimodality in waypoints& paths. To exemplify this dichotomy, we also propose a novel long term trajectory forecasting setting, with prediction horizons upto a minute, an order of magnitude longer than prior works. Finally, we presentY-net, a scene com-pliant trajectory forecasting network that exploits the pro-posed epistemic & aleatoric structure for diverse trajectory predictions across long prediction horizons.Y-net significantly improves previous state-of-the-art performance on both (a) The well studied short prediction horizon settings on the Stanford Drone & ETH/UCY datasets and (b) The proposed long prediction horizon setting on the re-purposed Stanford Drone & Intersection Drone datasets.
