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socialforce

Differentiable Social Force simulation with universal interaction potentials.

https://github.com/svenkreiss/socialforce

Science Score: 51.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ✓
    Committers with academic emails
    1 of 3 committers (33.3%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.5%) to scientific vocabulary

Keywords

differentiable-simulations social-force-model
Last synced: 7 months ago · JSON representation ·

Repository

Differentiable Social Force simulation with universal interaction potentials.

Basic Info
  • Host: GitHub
  • Owner: svenkreiss
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 68.6 MB
Statistics
  • Stars: 135
  • Watchers: 3
  • Forks: 43
  • Open Issues: 3
  • Releases: 3
Topics
differentiable-simulations social-force-model
Created over 7 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

Tests
Executable Book documentation.
Deep Social Force (arXiv:2109.12081).

Deep Social Force

Deep Social Force
Sven Kreiss, 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 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

Owner

  • Name: Sven Kreiss
  • Login: svenkreiss
  • Kind: user
  • Location: Switzerland

Computer vision. Background in particle physics.

Citation (citation.bib) 

@article{kreiss2021deep,
  title = {{Deep Social Force}},
  author = {Sven Kreiss},
  journal = {arXiv preprint arXiv:2109.12081},
  month = {September},
  year = {2021}
}

GitHub Events

Total
  • Watch event: 11
  • Fork event: 1
Last Year
  • Watch event: 11
  • Fork event: 1

Committers

Last synced: 10 months ago

All Time
  • Total Commits: 24
  • Total Committers: 3
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.083
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Sven Kreiss me@s****m 22
Parth Kothari 1****1 1
Sven Kreiss s****s@e****h 1
Committer Domains (Top 20 + Academic)
epfl.ch: 1 svenkreiss.com: 1

Issues and Pull Requests

Last synced: 9 months ago

All Time
  • Total issues: 3
  • Total pull requests: 8
  • Average time to close issues: about 22 hours
  • Average time to close pull requests: 3 months
  • Total issue authors: 3
  • Total pull request authors: 6
  • Average comments per issue: 3.0
  • Average comments per pull request: 0.88
  • Merged pull requests: 3
  • Bot issues: 0
  • Bot pull requests: 2
Past Year
  • Issues: 0
  • Pull requests: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 1
View more stats
Top Authors
Issue Authors
  • kawa-work (1)
  • RafalKucharskiPK (1)
  • Monk9636 (1)
Pull Request Authors
  • dependabot[bot] (3)
  • svenkreiss (2)
  • ChanganVR (1)
  • theDebugger811 (1)
  • kawa-work (1)
  • mabdn (1)
Top Labels
Issue Labels
Pull Request Labels
dependencies (3) github_actions (2)

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 23 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 4
  • Total maintainers: 1
pypi.org: socialforce

PyTorch implementation of DeepSocialForce.

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 23 Last month
Rankings
Forks count: 6.4%
Stargazers count: 6.6%
Dependent packages count: 10.1%
Average: 16.7%
Dependent repos count: 21.6%
Downloads: 38.8%
Maintainers (1)
svenkreiss
Last synced: 7 months ago

Dependencies

guide/requirements.txt pypi
  • jupyter-book *
  • matplotlib *
  • numpy *
setup.py pypi
  • numpy *
  • torch *