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Behavioral and semantic relationships play a vital role on intelligent self-driving vehicles and ADAS systems. Different from other research focused on trajectory, position, and bounding boxes, relationship data provides a human understandable description of the object&#x0027;s behavior, and it could describe an object&#x0027;s past and future status in an amazingly brief way. Therefore it is a fundamental method for tasks such as risk detection, environment understanding, and decision making. In this paper, we propose RSG-Net (Road Scene Graph Net): a graph convolutional network designed to predict potential semantic relationships from object proposals, and to produce a graph-structured result, called &#x201C;Road Scene Graph&#x201D;. The experimental results indicate that this network, trained on Road Scene Graph dataset, could efficiently predict potential semantic relationships among objects around the ego-vehicle.

RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments