mlsanityData to 14 Oct 2026

Towards Traffic Scene Description: The Semantic Scene Graph

arXiv 2111.10196 ↗First posted 19 Nov 2021cs.LGcited by 12

Not on the map. Why?

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.

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

10 of 30 references.

References · 10

  1. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
  2. 2020VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representationcited by 530
  3. 2020TNT: Target-driveN Trajectory Predictioncited by 347
  4. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
  5. 2019The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersectionscited by 137
  6. 2019Graph Neural Networks for Modelling Traffic Participant Interactioncited by 42
  7. 2017Ontology based Scene Creation for the Development of Automated Vehiclescited by 40
  8. 2021Description of Corner Cases in Automated Driving: Goals and Challengescited by 35
  9. 2020Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Frameworkcited by 4
  10. 2021Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Realitycited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 3; 2024: 3; 2025: 3; 2026: 1.

12 papers.