RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments
This addresses the need for human-understandable behavioral descriptions in autonomous driving systems, though it appears incremental as it builds on existing graph-based methods for relationship prediction.
The paper tackles the problem of predicting semantic relationships among objects for intelligent vehicles by proposing RSG-Net, a graph convolutional network that generates a Road Scene Graph, with experimental results showing it efficiently predicts potential relationships.
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's behavior, and it could describe an object'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 produces a graph-structured result, called "Road Scene Graph". 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.