https://github.com/bark-simulator/bark

Open-Source Framework for Development, Simulation and Benchmarking of Behavior Planning Algorithms for Autonomous Driving

Science Score: 33.0%

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

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
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DOI references
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Academic publication links
Links to: arxiv.org
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Committers with academic emails
2 of 26 committers (7.7%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (15.5%) to scientific vocabulary

Keywords

artificial-intelligence autonomous-driving autonomous-vehicles bark bark-simulator benchmark deep-reinforcement-learning machine-learning multi-agent reinforcement-learning research robotics self-driving-car simulation simulator verification

Keywords from Contributors

ode autograding sequences dataframe embedded graph-generation distributed projection annotation interactive

Last synced: 5 months ago · JSON representation

Repository

Open-Source Framework for Development, Simulation and Benchmarking of Behavior Planning Algorithms for Autonomous Driving

Basic Info

Host: GitHub
Owner: bark-simulator
License: mit
Language: C++
Default Branch: master
Homepage: https://bark-simulator.github.io/
Size: 13.3 MB

Statistics

Stars: 301
Watchers: 16
Forks: 72
Open Issues: 15
Releases: 6

Topics

Created almost 7 years ago · Last pushed about 2 years ago

Metadata Files

Readme Contributing License

README.md

BARK

$${\color{red}\text{BARK is not actively developed and maintained any longer.}}$$

$${\color{red}\text{Feel free to fork the repository and continue using BARK under the terms of the MIT license.}}$$

BARK - A Tool for Behavior benchmARKing

BARK is a semantic simulation framework for autonomous driving. Its behavior model-centric design allows for the rapid development, training, and benchmarking of various decision-making algorithms. It is especially suited for computationally expensive tasks, such as reinforcement learning. A a good starting point, have a look at the content of our BARK-Tutorial on IROS 2020.

Usage

(A) Pip Package

For whom it is: Python evangelists implementing python behavior models or ML scientists using BARK-ML for learning behaviors.

Bark is available as PIP-Package for Ubuntu and MacOS for Python>=3.7. You can install the latest version with pip install bark-simulator. The Pip package supports full benchmarking functionality of existing behavior models and development of your models within python.

After installing the package, you can have a look at the examples to check how to use BARK.

| Highway Example | Merging Example | Intersection Example | | --- | --- | --- | | | | |

(B) Build it from Source

For whom it is: C++ developers creating C++ behavior models, researchers performing benchmarks, or contributors to BARK.

Use git clone https://github.com/bark-simulator/bark.git or download the repository from this page. Then follow the instructions at How to Install BARK.

To get step-by-step instructions on how to use BARK, you can run our IPython Notebook tutorials using bazel run //docs/tutorials:run. For a more detailed understanding of how BARK works, its concept and use cases have a look at our documentation.

Example Benchmark is a running example of how to use BARK for benchmarking for scientific purposes.

Scientific Publications using BARK

BARK: Open Behavior Benchmarking in Multi-Agent Environments (IROS 2020)
Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments (IV 2020)
Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving (IROS 2020, PLC Workshop)
Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning (IROS 2020, PLC Workshop)
Formalizing Traffic Rules for Machine Interpretability (CAVS 2020)
Robust Stochastic Bayesian Games for Behavior Space Coverage (RSS 2020, Workshop on Interaction and Decision-Making in Autonomous-Driving)
Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving (IV 2021)
Risk-Based Safety Envelopes for Autonomous Vehicles Under Perception Uncertainty (Arxiv)

BARK Ecosystem

The BARK ecosystem is composed of multiple components that all share the common goal to develop and benchmark behavior models:

BARK-ML: Machine learning library for decision-making in autonomous driving.
BARK-MCTS: Integrates a template-based C++ Monte Carlo Tree Search Library into BARK to support development of both single- and multi-agent search methods.
BARK-Rules-MCTS: Integrates traffic rules within Monte Carlo Tree Search with lexicographic ordering.
BARK-MIQP: MINIVAN Planner based on MIQP for single- and multi-agent planning. Check out the build instructions.
BARK-DB: Provides a framework to integrate multiple BARK scenario sets into a database. The database module supports binary serialization of randomly generated scenarios to ensure exact reproducibility of behavior benchmarks across systems.
BARK-Rule-Monitoring: Provides runtime verification of Rules in Linear Temporal Logic (LTL) on simulated BARK traces.
CARLA-Interface: A two-way interface between CARLA and BARK. BARK behavior models can control CARLA vehicles. CARLA controlled vehicles are mirrored to BARK.

Paper

If you use BARK, please cite us using the following paper:

@inproceedings{Bernhard2020, title = {BARK: Open Behavior Benchmarking in Multi-Agent Environments}, author = {Bernhard, Julian and Esterle, Klemens and Hart, Patrick and Kessler, Tobias}, booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, url = {https://arxiv.org/pdf/2003.02604.pdf}, year = {2020} }

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

BARK specific code is distributed under MIT License.

Owner

Name: bark-simulator
Login: bark-simulator
Kind: organization

Repositories: 10
Profile: https://github.com/bark-simulator

GitHub Events

Total

Watch event: 17
Fork event: 3

Last Year

Watch event: 17
Fork event: 3

Committers

Last synced: about 2 years ago

All Time

Total Commits: 2,886
Total Committers: 26
Avg Commits per committer: 111.0
Development Distribution Score (DDS): 0.733

Past Year

Commits: 0
Committers: 0
Avg Commits per committer: 0.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
Patrick Hart	p**0@g**m	771
klemense1	e**e@f**g	717
juloberno	j**o@g**m	574
jbernhard	b**d@f**g	318
cirrostratus1	g**k@t**e	172
tin1254	t**4@h**m	118
Tobias Kessler	k**r@f**g	69
mansoorcheema	m**1@g**m	52
Andreas Kreutz	a**z@h**e	26
Klemens Esterle	k****1	15
Xiangzhong Liu	4****u	11
Klemens Esterle	k**e@p**e	8
Patrick Hart	h**t@f**l	6
Julian Bernhard	4****o	6
dependabot[bot]	4****]	5
Esra Acar-Celik	a**k@f**g	3
mansoorcheema	M****r	3
Thrash Abaddon	s**9@g**m	2
Bastian Hofmann	b**n@m**m	2
tobiaskessler	4****r	2
Amit Sahu	s**u@f**g	1
Bastian Hofmann	b**n@j**e	1
Christoph Schoeller	s**r@f**g	1
Daniel Bencic	d**c@p**m	1
grzpat	p**k@t**e	1
The Codacy Badger	b**r@c**m	1

Committer Domains (Top 20 + Academic)

fortiss.org: 6 tum.de: 2 codacy.com: 1 joblocal.de: 1 me.com: 1 posteo.de: 1 hotmail.de: 1

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 25
Total pull requests: 77
Average time to close issues: 4 months
Average time to close pull requests: 22 days
Total issue authors: 11
Total pull request authors: 10
Average comments per issue: 2.48
Average comments per pull request: 0.12
Merged pull requests: 60
Bot issues: 0
Bot pull requests: 11

Past Year

Issues: 0
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 0
Pull request authors: 0
Average comments per issue: 0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

tinmodeHuang (9)
mrzheng8 (5)
Rx-SUN (2)
juloberno (2)
valaxkong (1)
lenargasimov (1)
nivolog (1)
ppjtzjb (1)
tobiaskessler (1)
klemense1 (1)
flxebert (1)

Pull Request Authors

klemense1 (19)
patrickhart (19)
juloberno (15)
dependabot[bot] (11)
tobiaskessler (6)
CesarLiu (4)
daniel-bencic (1)
tinmodeHuang (1)
amitsahu7 (1)
cschoeller (1)

Top Labels

Issue Labels

enhancement (2) concept (1)

Pull Request Labels

dependencies (11)

Packages

Total packages: 2
Total downloads:
- pypi 594 last-month

Total dependent packages: 0
(may contain duplicates)
Total dependent repositories: 2
(may contain duplicates)
Total versions: 112
Total maintainers: 4

proxy.golang.org: github.com/bark-simulator/bark

Documentation: https://pkg.go.dev/github.com/bark-simulator/bark#section-documentation
License: mit
Latest release: v1.4.10
published almost 4 years ago

Versions: 65
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.7%

Last synced: 6 months ago

pypi.org: bark-simulator

A tool for Behavior benchmARKing

Homepage: https://github.com/bark-simulator/bark
Documentation: https://bark-simulator.readthedocs.io/
License: MIT
Latest release: 1.4.10
published almost 4 years ago

Versions: 47
Dependent Packages: 0
Dependent Repositories: 2
Downloads: 594 Last month

Rankings

Stargazers count: 4.0%

Forks count: 5.5%

Dependent packages count: 7.3%

Average: 7.6%

Downloads: 9.4%

Dependent repos count: 11.8%

Maintainers (4)

juloberno klemense1 mansoorcheema patrickhrt

Last synced: 6 months ago

Dependencies

setup.py pypi

aabbtree >=2.3.1
autopep8 >=1.4.4
cpplint >=1.4.4
ipython >=7.13.0
jupyter >=1.0.0
lxml >=4.4.2
matplotlib >=3.3.2
notebook >=6.0.3
numpy >=1.18.1
pandas >=0.24.2
psutil >=5.7.2
pygame >=1.9.6
ray >=1.3.0
scipy >=1.4.1
sphinx >=2.3.1
sphinx_rtd_theme >=0.4.3

tools/installers/requirements.txt pypi

aabbtree ==2.3.1
autopep8 ==1.4.4
cpplint ==1.4.4
importlib-metadata ==1.6.0
ipython ==7.13.0
jupyter ==1.0.0
lxml ==4.6.3
matplotlib ==3.3.2
notebook ==6.4.1
numpy ==1.18.1
panda3d ==1.10.5
pandas >=0.24.2
psutil ==5.7.2
pygame >=1.9.6
ray ==0.8.5
recommonmark ==0.6.0
scipy ==1.4.1
setuptools ==50.3.2
six ==1.15.0
sphinx ==2.3.1
sphinx_rtd_theme ==0.4.3
twine ==3.2.0
wheel ==0.35.1
xviz-avs ==0.1.0a4

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.github/workflows/build_wheels_39.yml actions

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.github/workflows/main.yml actions

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.github/workflows/nightly_ltl.yml actions

actions/checkout v1 composite

.github/workflows/nightly_rules_mcts.yml actions

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.github/workflows/rss.yml actions

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tools/Dockerfile docker

openjdk 8 build
python 3.7 build

tools/ci/Dockerfile docker

quay.io/pypa/manylinux2014_x86_64 latest build