Scenario-Transferable Semantic Graph Reasoning for Interaction-Aware Probabilistic Prediction

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. We first introduce generic representations for both static and dynamic elements in driving environments. Then these representations are utilized to describe semantic goals for selected agents and incorporate them into spatial-temporal structures. Finally, we reason internal relations among these structured semantic representations using learning-based method and obtain prediction results. The proposed algorithm is thoroughly examined under several complicated real-world driving scenarios to demonstrate its flexibility and transferability, where the predictor can be directly used under unforeseen driving circumstances with different static and dynamic information.