  As more and more autonomous vehicles (AVs) are being deployed on public
roads, designing socially compatible behaviors for them is becoming
increasingly important. In order to generate safe and efficient actions, AVs
need to not only predict the future behaviors of other traffic participants,
but also be aware of the uncertainties associated with such behavior
prediction. In this paper, we propose an uncertain-aware integrated prediction
and planning (UAPP) framework. It allows the AVs to infer the characteristics
of other road users online and generate behaviors optimizing not only their own
rewards, but also their courtesy to others, and their confidence regarding the
prediction uncertainties. We first propose the definitions for courtesy and
confidence. Based on that, their influences on the behaviors of AVs in
interactive driving scenarios are explored. Moreover, we evaluate the proposed
algorithm on naturalistic human driving data by comparing the generated
behavior against ground truth. Results show that the online inference can
significantly improve the human-likeness of the generated behaviors.
Furthermore, we find that human drivers show great courtesy to others, even for
those without right-of-way. We also find that such driving preferences vary
significantly in different cultures.
