mlsanityData to 7 Oct 2026

Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric

arXiv 2302.01259 ↗First posted 2 Feb 2023cs.LGcited by 4

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Abstract (arXiv)

Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure. With the recent advent of graph neural networks (GNNs) as the accompanying deep learning framework, the graph structure can be efficiently leveraged for various machine learning applications such as trajectory prediction. As a first of its kind, our proposed Python framework offers an easy-to-use and fully customizable data processing pipeline to extract standardized graph datasets from traffic scenarios. Providing a platform for GNN-based autonomous driving research, it improves comparability between approaches and allows researchers to focus on model implementation instead of dataset curation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 26 of its 42 references that are in the map. First posted on arXiv 2 Feb 2023.

Builds on

26 of 42 references.

References · 26

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14 more
  1. 2015Structural-RNN: Deep Learning on Spatio-Temporal Graphscited by 234
  2. 2019INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Mapscited by 231
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  4. 2021GOHOME: Graph-Oriented Heatmap Output for future Motion Estimationcited by 146
  5. 2021LaneRCNN: Distributed Representations for Graph-Centric Motion Forecastingcited by 112
  6. 2019GRIP++: Enhanced Graph-based Interaction-aware Trajectory Prediction for Autonomous Drivingcited by 73
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  9. 2021Trajectory Prediction with Graph-based Dual-scale Context Fusioncited by 19
  10. 2020SCALE-Net: Scalable Vehicle Trajectory Prediction Network under Random Number of Interacting Vehicles via Edge-enhanced Graph Convolutional Neural Networkcited by 18
  11. 2019Graph Convolutional Networks for Road Networkscited by 17
  12. 2019Dynamic Interaction-Aware Scene Understanding for Reinforcement Learning in Autonomous Drivingcited by 14
  13. 2020Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environmentscited by 13
  14. 2021StarNet: Joint Action-Space Prediction with Star Graphs and Implicit Global Frame Self-Attentioncited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 2; 2026: 1.

4 papers.

  1. 2023Deep Occupancy-Predictive Representations for Autonomous Drivingcited by 1
  2. 2025Predictive Traffic Rule Compliance using Reinforcement Learningcited by 1
  3. 2025Exploring Semantic Clustering and Similarity Search for Heterogeneous Traffic Scenario Graphcited by 0
  4. 2026From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Drivingcited by 0