As more and more autonomous vehicles (AVs) are being deployed on public roads, designing socially compatible behaviors for them is of critical importance. Based on observations , AVs need to  predict the future behaviors of other traffic participants, and be aware of the uncertainties associated with such prediction so that safe, efficient, and human-like motions can be generated . In this paper, we propose an  integrated prediction and planning framework that allows the AVs to online infer the characteristics of other road users  and generate behaviors optimizing not only their own rewards, but also their courtesy to others, as well as their confidence on the consequences in the presence of uncertainties. Based on the definitions of courtesy and confidence , we explore the influences of such factors on the behaviors of AVs in interactive driving scenarios  . Moreover, we evaluate the proposed algorithm on naturalistic human driving data by comparing the generated behavior with the 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  .
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 .
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