  When autonomous vehicles still struggle to solve challenging situations
during on-road driving, humans have long mastered the essence of driving with
efficient transferable and adaptable driving capability. By mimicking humans'
cognition model and semantic understanding during driving, we present HATN, a
hierarchical framework to generate high-quality driving behaviors in
multi-agent dense-traffic environments. Our method hierarchically consists of a
high-level intention identification and low-level action generation policy.
With the semantic sub-task definition and generic state representation, the
hierarchical framework is transferable across different driving scenarios.
Besides, our model is also able to capture variations of driving behaviors
among individuals and scenarios by an online adaptation module. We demonstrate
our algorithms in the task of trajectory prediction for real traffic data at
intersections and roundabouts, where we conducted extensive studies of the
proposed method and demonstrated how our method outperformed other methods in
terms of prediction accuracy and transferability.
