Automated vehicles (AVs) have already been operating in cities. However, there is a significant lack of understanding of the social interactions between AVs and pedestrians at real uncontrolled intersections. Particularly, how do human drivers control vehicle dynamics to convey specific social values to pedestrians and passengers? To minimize this research gap, this study has conducted an in-depth analysis of pedestrian-vehicle interaction trajectory data at real uncontrolled mid-block intersections by combining vehicle dynamic parameters with pedestrian behavior, using the proposed Time-to-Conflict Point (TTCP) and Time-to-Arrival (TTA) diagrams. Based on the theory of social value orientation, the social compliant driving behaviors of vehicles are categorized into five types: egoistic, competitive (successful), competitive (unsuccessful), prosocial, and altruistic. Our results show that, in real scenarios, most driving behaviors fall under the egoistic and competitive (successful) modes, with the altruistic mode having the lowest percentage. This paper discusses the significance of the proposed quantitative methods and their research results in understanding social compliant interactive driving behaviors.