论文标题
UST：自动驾驶中轨迹预测的时空上下文统一上下文
UST: Unifying Spatio-Temporal Context for Trajectory Prediction in Autonomous Driving
论文作者
论文摘要
对于自主驾驶来说，轨迹预测一直是一个具有挑战性的问题，因为它需要从交通参与者的行为和互动中推断出潜在意图。这个问题本质上是困难的，因为每个参与者在不同的环境和互动下的行为可能会有所不同。该关键是有效地对空间上下文和时间上下文的相互隔行影响进行建模。现有的工作通常分别编码这两种类型的上下文，这将导致场景的劣等建模。在本文中，我们首先提出了一种统一的方法，以平等地对时空的时间和空间维度进行建模。提出的模块在几行代码内易于实现。与现有的方法相反，这些方法在很大程度上依赖于空间上下文的时间上下文和手工制作结构的复发性神经网络，我们的方法可以自动将时空空间分开以适应数据。最后，我们在最近提出的两个轨迹预测数据集Apolloscape和Argoverse上测试了我们提出的框架。我们表明，所提出的方法在保持其简单性的同时大大优于先前的最新方法。这些令人鼓舞的结果进一步验证了我们方法的优势。
Trajectory prediction has always been a challenging problem for autonomous driving, since it needs to infer the latent intention from the behaviors and interactions from traffic participants. This problem is intrinsically hard, because each participant may behave differently under different environments and interactions. This key is to effectively model the interlaced influence from both spatial context and temporal context. Existing work usually encodes these two types of context separately, which would lead to inferior modeling of the scenarios. In this paper, we first propose a unified approach to treat time and space dimensions equally for modeling spatio-temporal context. The proposed module is simple and easy to implement within several lines of codes. In contrast to existing methods which heavily rely on recurrent neural network for temporal context and hand-crafted structure for spatial context, our method could automatically partition the spatio-temporal space to adapt the data. Lastly, we test our proposed framework on two recently proposed trajectory prediction dataset ApolloScape and Argoverse. We show that the proposed method substantially outperforms the previous state-of-the-art methods while maintaining its simplicity. These encouraging results further validate the superiority of our approach.
