mlsanityData to 7 Oct 2026

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

arXiv 2207.12321 ↗First posted 16 Jul 2022cs.CV cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 27 of its 35 references that are in the map. First posted on arXiv 16 Jul 2022.

Builds on

27 of 35 references.

References · 27

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15 more
  1. 2016Visual Relationship Detection with Language Priorscited by 333
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  15. 2020Understanding Dynamic Scenes using Graph Convolution Networkscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 1.

2 papers.

  1. 2023Interpretable End-to-End Driving Model for Implicit Scene Understandingcited by 2
  2. 2026Hazard-Aware Traffic Scene Graph Generationcited by 0