Socially-Compatible Behavior Design of Autonomous Vehicles with Verification on Real Human Data
This work addresses the challenge of making autonomous vehicles behave more like human drivers in interactive scenarios, which is crucial for safe and efficient deployment on public roads, though it is incremental as it builds on existing prediction and planning methods.
The paper tackles the problem of designing socially-compatible behaviors for autonomous vehicles by proposing an uncertain-aware integrated prediction and planning framework that improves human-likeness in generated behaviors, with results showing significant enhancement in human-likeness and insights into cultural variations in driving courtesy.
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.