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arXiv · 2207.12321

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

Yafu Tian·Alexander Carballo·Ruifeng Li·Kazuya Takeda
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Abstract

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.

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Yafu Tian, Alexander Carballo, Ruifeng Li, Kazuya Takeda. 2022-07-16. RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments. https://doi.org/10.1109/iv48863.2021.9575491

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