Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, deployment of intelligent transport systems, traffic operations, to name a few. Early works handcrafted this representation based on domain knowledge. However, social interactions in crowded environments are not only diverse but often subtle. Recently, deep learning methods have outperformed their handcrafted counterparts, as they learned about human-human interactions in a more generic data-driven fashion. In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two knowledge-based data-driven methods to effectively capture these social interactions. To objectively compare the performance of these interaction-based forecasting models, we develop a large scale interaction-centric benchmark TrajNet++, a significant yet missing component in the field of human trajectory forecasting. We propose novel performance metrics that evaluate the ability of a model to output socially acceptable trajectories. Experiments on TrajNet++ validate the need for our proposed metrics, and our method outperforms competitive baselines on both real-world and synthetic datasets.
翻译:过去几十年以来,人类轨迹预测一直是一个积极研究的领域,原因是其无数实际应用:疏散情况分析、智能运输系统的部署、交通业务等等。早期工作根据领域知识手工制作了这种代表。然而,拥挤环境中的社会互动不仅多样化,而且往往微妙。最近,深层学习方法在以更通用的数据驱动方式学习人与人之间的相互作用方面表现优于他们的手工设计对应方。在这项工作中,我们对现有基于深层次学习的模拟社会互动的方法进行了深入分析。我们提出了两种基于知识的数据驱动方法,以有效捕捉这些社会互动。为了客观地比较这些基于互动的预测模型的性能,我们开发了一个大型互动中心基准TrajNet++,这是人类轨迹预测领域一个重要但缺失的组成部分。我们提出了新的性能衡量标准,用以评价模型在社会上可接受轨迹的输出能力。关于TrajNet++的实验证实了我们拟议指标的必要性,我们的方法在现实世界和合成数据集上都超过了竞争性基准。