Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios

This repository provides the architecture and training as presented in "Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios".

Setup and Train the Network

1. Clone the repository.
2. Download the data from https://faubox.rrze.uni-erlangen.de/getlink/fiXiigoqRjsuHSuCQeNXzx4t/Expert-LaSTS_data.zip and unzip it into a folder \data, rooted in the project directory.
3. Run python main.py to train the model. Adjust parameters as required in main.py.

Sources

This repository is based on the former version https://github.com/JWTHI/ViTAL-SCENE. The implementation is changed to handle dynamic infroamtion aside of the formerly used infrastructure information. The Vision-Transformer implementation is realized through vit_pytorch.py, provided in [vit-pytorch] (https://github.com/lucidrains/vit-pytorch).

Citation

If you are using this repository, please cite the work

@InProceedings{Wurst2022a,
  author    = {Jonas {Wurst} and Lakshman {Balasubramanian} and Michael {Botsch} and Wolfgang {Utschick}},
  title     = {Expert-LaSTS: Expert-Knowledge Guided Latent Space for Traffic Scenarios},
  booktitle = {2022 IEEE Intelligent Vehicles Symposium (IV)},
  year      = {2022},
}