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.
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}}
SCENE is licensed under Creative Commons Attribution-NonCommercial 4.0 International License.
Check LICENSE for more information.
We recommend using Anaconda.
The installation is described on the following page:
https://docs.anaconda.com/anaconda/install/linux/
conda env create -f environment.ymlconda activate scenepython3 main.py --dataset=aifbOptions for --dataset are aifb, mutag, bgs and am.
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.