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

作者：Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda · 年份：2021 · DOI：10.1109/iv48863.2021.9575491 · 被引用次数：9 · 研究领域：Multimodal Machine Learning Applications、Advanced Graph Neural Networks、Topic Modeling

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 to produce 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.