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.

翻译：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来自两个来源:(a) 代理人已知但模型未知的来源,例如长期目标和(b) 代理人和模型都未知的来源,例如其他代理人的意图和不可复制的随机性,这是人类轨迹预测的内在多式问题。我们提议将这种不确定性纳入其缩略图和解析源中。我们通过长期目标的多式联运和通过路点和路径的多式联运的解析性不确定性来模拟隐性不确定性。为举例说明这一二分法,我们还提出一个新的长期轨迹预测设置,预测地平面可达一分钟,其规模可达比先前工作长。最后,我们介绍Y-net,一个场景可访问轨迹预测网络,利用亲现的缩略图和解析性结构,对长期预测地平面的不同轨迹预测进行模拟。Y-net显著改进了先前的状态和多端点的多端点和多端预测。