# Towards Traffic Scene Description: The Semantic Scene Graph

arXiv: https://arxiv.org/abs/2111.10196 · HTML: https://mlsanity.com/paper/2111.10196/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [TUDataset: A collection of benchmark datasets for learning with graphs](https://mlsanity.com/paper/2007.08663/) (2020)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps](https://mlsanity.com/paper/1910.03088/) (2019)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Graph Neural Networks for Modelling Traffic Participant Interaction](https://mlsanity.com/paper/1903.01254/) (2019)
- [Ontology based Scene Creation for the Development of Automated Vehicles](https://mlsanity.com/paper/1704.01006/) (2017)
- [Description of Corner Cases in Automated Driving: Goals and Challenges](https://mlsanity.com/paper/2109.09607/) (2021)
- [Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework](https://mlsanity.com/paper/2001.11507/) (2020)
- [Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality](https://mlsanity.com/paper/2105.01610/) (2021)

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

- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [Enhancing Vision-Language Models with Scene Graphs for Traffic Accident Understanding](https://mlsanity.com/paper/2407.05910/) (2024)
- [Relation-based Motion Prediction using Traffic Scene Graphs](https://mlsanity.com/paper/2212.02503/) (2022)
- [Traffic Scene Similarity: a Graph-based Contrastive Learning Approach](https://mlsanity.com/paper/2309.09720/) (2023)
- [SocialFormer: Social Interaction Modeling with Edge-enhanced Heterogeneous Graph Transformers for Trajectory Prediction](https://mlsanity.com/paper/2405.03809/) (2024)
- [GOTPR: General Outdoor Text-based Place Recognition Using Scene Graph Retrieval with OpenStreetMap](https://mlsanity.com/paper/2501.08575/) (2025)
- [Heterogeneous Graph-based Trajectory Prediction using Local Map Context and Social Interactions](https://mlsanity.com/paper/2311.18553/) (2023)
- [Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation](https://mlsanity.com/paper/2402.03840/) (2024)
- [Self Supervised Clustering of Traffic Scenes using Graph Representations](https://mlsanity.com/paper/2211.15508/) (2022)
- [Learning Isometric Embeddings of Road Networks using Multidimensional Scaling](https://mlsanity.com/paper/2504.17534/) (2025)
- [Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graph](https://mlsanity.com/paper/2507.05086/) (2025)
- [hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freeways](https://mlsanity.com/paper/2606.27577/) (2026)

## Abstract (arXiv)

For the classification of traffic scenes, a description model is necessary that can describe the scene in a uniform way, independent of its domain. A model to describe a traffic scene in a semantic way is described in this paper. The description model allows to describe a traffic scene independently of the road geometry and road topology. Here, the traffic participants are projected onto the road network and represented as nodes in a graph. Depending on the relative location between two traffic participants with respect to the road topology, semantically classified edges are created between the corresponding nodes. For concretization, the edge attributes are extended by relative distances and velocities between both traffic participants with regard to the course of the lane. An important aspect of the description is that it can be converted easily into a machine-readable format. The current description focuses on dynamic objects of a traffic scene and considers traffic participants, such as pedestrians or vehicles.