This python module contains scripts needed to automatically extract similar scenes from naturalistic traffic datasets. This code accompanies the paper "Automatic extraction of similar traffic scenes from large naturalistic datasets using the Hausdorff distance - Siebinga, Zgonnikov & Abbink 2022".
This module cannot be used on its own, its should be used as a sub-module of the TraViA visualization software (click here for the paper, or here for the repository). If you want to use this module, first make sure you have a working version of TraViA. You can clone it directly from github using the following command, or fork it first and clone your own version.
git clone https://github.com/tud-hri/travia.git
After cloning TraViA, you can navigate to the travia folder (cd travia) and clone this repository as a submodule. Use the following command to clone the
github version, or create a fork first and then clone your own fork.
git submodule add https://github.com/tud-hri/hausdorffsceneextraction.git
This submodule has some additional dependencies besides the dependencies of TraViA itself. Please make sure to install all dependencies by running the commands below.
pip install -r requirements.txt
cd hausdorffsceneextraction
pip install -r requirements.txt
Instruction on how to get the data and how to work with TraViA can be found in the TraViA README file. See the instructions below for how to work with this sub-module.
Run the script extract_scenes.py to find situations with similar traffic context to a specific scenario. First select an example of the situation you
are interested in. You can do this using TraViA itself. Now use the selected dataset id, vehicle id, and frame number in the main block of
extract_scenes.py. You can use the parameter datasets_to_search to exclude part of the highD dataset and speed things up. Finally, you can alter the
arguments in the function post_process to determine which plots to generate.