Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction
This work addresses the challenge of autonomous vehicle navigation in complex multi-agent environments, offering a transferable and adaptable solution for driving behavior prediction.
The paper tackles the problem of predicting driving behaviors in dense traffic by introducing HATN, a hierarchical framework that mimics human cognition to generate high-quality trajectories, demonstrating improved prediction accuracy and transferability in real traffic data at intersections and roundabouts.
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.