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Accurately predicting the possible behaviors of traffic participants is anessential capability for autonomous vehicles. Since autonomous vehicles need tonavigate in dynamically changing environments, they are expected to makeaccurate predictions regardless of where they are and what drivingcircumstances they encountered. Several methodologies have been proposed tosolve prediction problems under different traffic situations. These worksusually combine agent trajectories with either color-coded or vectorized highdefinition (HD) map as input representations and encode this information forbehavior prediction tasks. However, not all the information is relevant in thescene for the forecasting and such irrelevant information may be evendistracting to the forecasting in certain situations. Therefore, in this paper,we propose a novel generic representation for various driving environments bytaking the advantage of semantics and domain knowledge. Using semantics enablessituations to be modeled in a uniform way and applying domain knowledge filtersout unrelated elements to target vehicle's future behaviors. We then propose ageneral semantic behavior prediction framework to effectively utilize theserepresentations by formulating them into spatial-temporal semantic graphs andreasoning internal relations among these graphs. We theoretically andempirically validate the proposed framework under highly interactive andcomplex scenarios, demonstrating that our method not only achievesstate-of-the-art performance, but also processes desirable zero-shottransferability.

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