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

• URL: http://arxiv.org/abs/2207.12321v1
• Date: Sat, 16 Jul 2022 12:40:17 GMT
• Title: RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments
• Authors: Yafu Tian, Alexander Carballo, Ruifeng Li and Kazuya Takeda
• Abstract summary: We propose RSG-Net (Road Scene Graph Net): a graph convolutional network designed to predict potential semantic relationships from object proposals. 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.
• Score: 72.04891523115535
• License: http://creativecommons.org/licenses/by/4.0/
• 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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