Metadata-Version: 2.1
Name: socialforce
Version: 0.2.3
Summary: PyTorch implementation of DeepSocialForce.
Home-page: https://github.com/svenkreiss/socialforce
Author: Sven Kreiss
Author-email: research@svenkreiss.com
License: MIT
Description: [![Tests](https://github.com/svenkreiss/socialforce/actions/workflows/tests.yml/badge.svg)](https://github.com/svenkreiss/socialforce/actions/workflows/tests.yml)<br />
        [Executable Book documentation](https://www.svenkreiss.com/socialforce/).<br />
        [Deep Social Force (arXiv:2109.12081)](https://arxiv.org/abs/2109.12081).
        
        # Deep Social Force
        
        > [__Deep Social Force__](https://arxiv.org/abs/2109.12081)<br />
        > _[Sven Kreiss](https://www.svenkreiss.com)_, 2021.
        >
        > The Social Force model introduced by Helbing and Molnar in 1995
        > is a cornerstone of pedestrian simulation. This paper
        > introduces a differentiable simulation of the Social Force model
        > where the assumptions on the shapes of interaction potentials are relaxed
        > with the use of universal function approximators in the form of neural
        > networks.
        > Classical force-based pedestrian simulations suffer from unnatural
        > locking behavior on head-on collision paths. In addition, they cannot
        > model the bias
        > of pedestrians to avoid each other on the right or left depending on
        > the geographic region.
        > My experiments with more general interaction potentials show that
        > potentials with a sharp tip in the front avoid
        > locking. In addition, asymmetric interaction potentials lead to a left or right
        > bias when pedestrians avoid each other.
        
        
        # Install and Run
        
        ```sh
        # install from PyPI
        pip install 'socialforce[dev,plot]'
        
        # or install from source
        pip install -e '.[dev,plot]'
        
        # run linting and tests
        pylint socialforce
        pycodestyle socialforce
        pytest tests/*.py
        ```
        
        
        # Ped-Ped-Space Scenarios
        
        <img src="docs/separator.gif" height=200 />
        <img src="docs/gate.gif" height=200 />
        
        Emergent lane forming behavior with 30 and 60 pedestrians:
        
        <img src="docs/walkway_30.gif" height=200 />
        <img src="docs/walkway_60.gif" height=200 />
        
        
        # Download TrajNet++ Data
        
        The [Executable Book](https://www.svenkreiss.com/socialforce/)
        requires some real-world data for the TrajNet++ section.
        This is how to download and unzip it to the right folder:
        
        ```
        wget -q https://github.com/vita-epfl/trajnetplusplusdata/releases/download/v4.0/train.zip
        mkdir data-trajnet
        unzip train.zip -d data-trajnet
        ```
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: PyPy
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: plot
