https://github.com/svenkreiss/socialforce

Differentiable Social Force simulation with universal interaction potentials.
https://github.com/svenkreiss/socialforce

differentiable-simulations social-force-model

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Differentiable Social Force simulation with universal interaction potentials.

• Host: GitHub
• URL: https://github.com/svenkreiss/socialforce
• Owner: svenkreiss
• License: mit
• Created: 2018-08-13T12:10:04.000Z (over 6 years ago)
• Default Branch: main
• Last Pushed: 2024-09-03T22:20:56.000Z (4 months ago)
• Last Synced: 2024-10-13T00:10:16.465Z (3 months ago)
• Topics: differentiable-simulations, social-force-model
• Language: Jupyter Notebook
• Homepage:
• Size: 68.6 MB
• Stars: 124
• Watchers: 4
• Forks: 42
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Citation: citation.bib

Awesome Lists containing this project

README

        
[![Tests](https://github.com/svenkreiss/socialforce/actions/workflows/tests.yml/badge.svg)](https://github.com/svenkreiss/socialforce/actions/workflows/tests.yml)


[Executable Book documentation](https://www.svenkreiss.com/socialforce/).


[Deep Social Force (arXiv:2109.12081)](https://arxiv.org/abs/2109.12081).

# Deep Social Force

> [__Deep Social Force__](https://arxiv.org/abs/2109.12081)


> _[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





Emergent lane forming behavior with 30 and 60 pedestrians:





# 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

```