Recent advances in trajectory prediction have shown that explicit reasoning about agents' intent is important to accurately forecast their motion. However, the current research activities are not directly applicable to intelligent and safety critical systems. This is mainly because very few public datasets are available, and they only consider pedestrian-specific intents for a short temporal horizon from a restricted egocentric view. To this end, we propose LOKI (LOng term and Key Intentions), a novel large-scale dataset that is designed to tackle joint trajectory and intention prediction for heterogeneous traffic agents (pedestrians and vehicles) in an autonomous driving setting. The LOKI dataset is created to discover several factors that may affect intention, including i) agent's own will, ii) social interactions, iii) environmental constraints, and iv) contextual information. We also propose a model that jointly performs trajectory and intention prediction, showing that recurrently reasoning about intention can assist with trajectory prediction. We show our method outperforms state-of-the-art trajectory prediction methods by upto $27\%$ and also provide a baseline for frame-wise intention estimation.
翻译:最近的轨迹预测进展表明,关于代理人意图的明确推理对于准确预测其运动十分重要,但是,目前的研究活动并不直接适用于智能和安全关键系统,这主要是因为很少有公共数据集可供使用,它们只从有限的自我中心观点考虑短时间跨度的行人特有意图。为此,我们提议Loki(Long术语和关键意图),这是一个新的大型数据集,旨在解决在自主驾驶环境下对各种交通剂(旅客和车辆)的联合轨迹和意向预测问题。Loki数据集的创建是为了发现可能影响意图的若干因素,包括i) 代理人自己的意志、ii) 社会互动、iii) 环境制约因素和iv) 背景资料。我们还提出了一个模型,共同进行轨迹和意图预测,表明对意图的反复推理有助于轨迹预测。我们用27.%美元显示我们的方法超越了最新轨迹预测方法,并为框架意图估计提供了一个基线。