Socially Compatible Control Design of Automated Vehicle in Mixed Traffic

Abstract

In the car-following scenarios, automated vehicles (AVs) usually plan motions without considering the impacts of their actions on the following human drivers. This paper aims to leverage such impacts to plan more efficient and socially desirable AV behaviors in human-AV interactions. Specifically, we introduce a socially compatible control design for the AV that benefits mixed traffic in the car-following scenarios. The proposed design enables the altruistic AV in human-AV interaction by integrating the social value orientation from psychology into its decision-making process. The altruistic AV generates socially desirable behaviors by optimizing both its own reward and courtesy to the following human driver’s original plan in the longitudinal motion. The results show that as compared to the egoistic AV, the altruistic AV significantly avoids disrupting the following human driver’s initial plan and leads the following human driver to achieve considerably smaller car-following gap distance and time headway. Moreover, we investigated the impacts of the socially compatible control design with different altruism levels of the AV using statistical assessments. The results collectively demonstrate the significant improvement in traffic-level metrics as a result of the AV’s altruistic behaviors in human-AV interactions.

Publication
In Proceedings of the 2022 American Control Conference
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Mehmet Ozkan
Ph.D. Student
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