  Accurately predicting future behaviors of surrounding vehicles is an
essential capability for autonomous vehicles in order to plan safe and feasible
trajectories. The behaviors of others, however, are full of uncertainties. Both
rational and irrational behaviors exist, and the autonomous vehicles need to be
aware of this in their prediction module. The prediction module is also
expected to generate reasonable results in the presence of unseen and corner
scenarios. Two types of prediction models are typically used to solve the
prediction problem: learning-based model and planning-based model.
Learning-based model utilizes real driving data to model the human behaviors.
Depending on the structure of the data, learning-based models can predict both
rational and irrational behaviors. But the balance between them cannot be
customized, which creates challenges in generalizing the prediction results.
Planning-based model, on the other hand, usually assumes human as a rational
agent, i.e., it anticipates only rational behavior of human drivers. In this
paper, a generic prediction architecture is proposed to address various
rationalities in human behavior. We leverage the advantages from both
learning-based and planning-based prediction models. The proposed approach is
able to predict continuous trajectories that well-reflect possible future
situations of other drivers. Moreover, the prediction performance remains
stable under various unseen driving scenarios. A case study under a real-world
roundabout scenario is provided to demonstrate the performance and capability
of the proposed prediction architecture.
