# Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles

arXiv: https://arxiv.org/abs/2011.13588 · HTML: https://mlsanity.com/paper/2011.13588/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 12 of 12)

- [OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping](https://mlsanity.com/paper/2304.10440/) (2023)
- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2301.03512/) (2023)
- [RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding](https://mlsanity.com/paper/2304.08600/) (2023)
- [roadscene2vec: A Tool for Extracting and Embedding Road Scene-Graphs](https://mlsanity.com/paper/2109.01183/) (2021)
- [DROID: Driver-centric Risk Object Identification](https://mlsanity.com/paper/2106.13201/) (2021)
- [RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments](https://mlsanity.com/paper/2207.12321/) (2022)
- [DRIVESPATIAL: A Benchmark for Spatiotemporal Intelligence in VLMs for Autonomous Driving](https://mlsanity.com/paper/2605.23176/) (2026)
- [DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding](https://mlsanity.com/paper/2311.06497/) (2023)
- [Adversarial Patch Generation for Visual-Infrared Dense Prediction Tasks via Joint Position-Color Optimization](https://mlsanity.com/paper/2603.00266/) (2026)
- [A Two-Stage Framework for Ego-Centric Key Object Identification via Object State Prediction](https://mlsanity.com/paper/2609.07125/) (2026)

## Abstract (arXiv)

Rich semantic information extraction plays a vital role on next-generation intelligent vehicles. Currently there is great amount of research focusing on fundamental applications such as 6D pose detection, road scene semantic segmentation, etc. And this provides us a great opportunity to think about how shall these data be organized and exploited. In this paper we propose road scene graph,a special scene-graph for intelligent vehicles. Different to classical data representation, this graph provides not only object proposals but also their pair-wise relationships. By organizing them in a topological graph, these data are explainable, fully-connected, and could be easily processed by GCNs (Graph Convolutional Networks). Here we apply scene graph on roads using our Road Scene Graph dataset, including the basic graph prediction model. This work also includes experimental evaluations using the proposed model.