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SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks

Official repository of the paper:
SCENE: Reasoning about Traffic Scenes using Heterogeneous Graph Neural Networks
Thomas Monninger*, Julian Schmidt*, Jan Rupprecht, David Raba, Julian Jordan, Daniel Frank, Steffen Staab and Klaus Dietmayer
*Thomas Monninger and Julian Schmidt are co-first authors. The order was determined alphabetically.

IEEE Robotics and Automation Letters (RA-L), 2023

The repository contains the source code of our graph convolution operator and our experiments on publicly available knowledge graph datasets.

Citation

If you use our source code, please cite:

@Article{monningerschmidt2023scene,
  author={Monninger, Thomas and Schmidt, Julian and Rupprecht, Jan and Raba, David and Jordan, Julian and Frank, Daniel and Staab, Steffen and Dietmayer, Klaus},
  journal={IEEE Robotics and Automation Letters}, 
  title={SCENE: Reasoning About Traffic Scenes Using Heterogeneous Graph Neural Networks}, 
  year={2023},
  volume={8},
  number={3},
  pages={1531--1538},
  doi={10.1109/LRA.2023.3234771}}

License

Creative Commons License
SCENE is licensed under Creative Commons Attribution-NonCommercial 4.0 International License.

Check LICENSE for more information.

Installation

Install Anaconda

We recommend using Anaconda. The installation is described on the following page:
https://docs.anaconda.com/anaconda/install/linux/

Install Required Packages

conda env create -f environment.yml

Activate Environment

conda activate scene

Generate Results

python3 main.py --dataset=aifb

Options for --dataset are aifb, mutag, bgs and am.

Results

Results are stored in the results/ folder. By default, it contains the original results obtained on our test system.
Values are reported in our paper.
Test system specifications: Intel Core i9-7920X, NVIDIA GeForce RTX 2080 Ti.