论文标题
RSG-NET：在复杂环境中智能车辆的丰富的语义关系预测
RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments
论文作者
论文摘要
行为和语义关系在智能的自动驾驶汽车和ADAS系统上起着至关重要的作用。与其他针对轨迹，位置和边界框的研究不同，关系数据提供了对对象行为的可理解描述，并且可以以惊人的简短方式描述对象的过去和未来状态。因此，这是用于诸如风险检测，环境理解和决策等任务的基本方法。在本文中，我们提出了RSG-NET（Road Scene Graph Net）：旨在预测对象建议的潜在语义关系的图形卷积网络，并产生图形结构化结果，称为“ Road Scene Graph”。实验结果表明，该网络在道路场景图数据集中受过训练，可以有效地预测自我车辆周围对象之间的潜在语义关系。
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.
