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In the car-following scenarios, automated vehicles (AVs) usually plan motionswithout considering the impacts of their actions on the following humandrivers. This paper aims to leverage such impacts to plan more efficient andsocially desirable AV behaviors in human-AV interactions. Specifically, weintroduce a socially compatible control design for the AV that benefits mixedtraffic in the car-following scenarios. The proposed design enables thealtruistic AV in human-AV interaction by integrating the social valueorientation from psychology into its decision-making process. The altruistic AVgenerates socially desirable behaviors by optimizing both its own reward andcourtesy to the following human driver's original plan in the longitudinalmotion. The results show that as compared to the egoistic AV, the altruistic AVsignificantly avoids disrupting the following human driver's initial plan andleads the following human driver to achieve considerably smaller car-followinggap distance and time headway. Moreover, we investigated the impacts of thesocially compatible control design with different altruism levels of the AVusing statistical assessments. The results collectively demonstrate thesignificant improvement in traffic-level metrics as a result of the AV'saltruistic behaviors in human-AV interactions.

Socially Compatible Control Design of Automated Vehicle in Mixed Traffic