Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. A number of methodologies have been proposed to solve prediction problems under different traffic situations. However, these works either focus on one particular driving scenario (e.g. highway, intersection, or roundabout) or do not take sufficient environment information (e.g. road topology, traffic rules, and surrounding agents) into account. In fact, the limitation to certain scenario is mainly due to the lackness of generic representations of the environment. The insufficiency of environment information further limits the flexibility and transferability of the predictor. In this paper, we propose a scenario-transferable and interaction-aware probabilistic prediction algorithm based on semantic graph reasoning, which predicts behaviors of selected agents. We put forward generic representations for various environment information and utilize them as building blocks to construct their spatio-temporal structural relations. We then take the advantage of these structured representations to develop a flexible and transferable prediction algorithm, where the predictor can be directly used under unforeseen driving circumstances that are completely different from training scenarios. The proposed algorithm is thoroughly examined under several complicated real-world driving scenarios to demonstrate its flexibility and transferability with the generic representation for autonomous driving systems.

中文翻译：

---

用于交互感知概率预测的场景可转移语义图推理

准确预测交通参与者可能的行为是自动驾驶汽车必不可少的能力。由于自动驾驶汽车需要在动态变化的环境中导航，因此无论它们身在何处以及遇到何种驾驶环境，都需要做出准确的预测。已经提出了许多方法来解决不同交通情况下的预测问题。然而，这些工作要么专注于一种特定的驾驶场景（例如高速公路、交叉路口或环形交叉路口），要么没有考虑足够的环境信息（例如道路拓扑、交通规则和周围的代理）。事实上，对某些场景的限制主要是由于缺乏对环境的通用表示。环境信息的不足进一步限制了预测器的灵活性和可转移性。在本文中，我们提出了一种基于语义图推理的场景可转移和交互感知概率预测算法，该算法预测选定代理的行为。我们提出了各种环境信息的通用表示，并利用它们作为构建块来构建它们的时空结构关系。然后，我们利用这些结构化表示来开发灵活且可转移的预测算法，其中预测器可以在与训练场景完全不同的不可预见的驾驶情况下直接使用。