Socially-Compatible Behavior Design of Autonomous Vehicles With Verification on Real Human Data

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 letter, 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.

Automated summary

With the increasing deployment of autonomous vehicles on public roads, designing socially compatible behaviors with other traffic participants becomes crucial. The proposed uncertain-aware integrated prediction and planning framework allows AVs to infer the characteristics of other road users online and generate behaviors optimizing rewards, courtesy, and confidence regarding uncertainties. Research shows significant variations in driving preferences, such as courtesy towards others, among human drivers in different cultures.