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https://github.com/bark-simulator/bark
Open-Source Framework for Development, Simulation and Benchmarking of Behavior Planning Algorithms for Autonomous Driving
https://github.com/bark-simulator/bark
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
Last synced: about 1 month ago
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Open-Source Framework for Development, Simulation and Benchmarking of Behavior Planning Algorithms for Autonomous Driving
- Host: GitHub
- URL: https://github.com/bark-simulator/bark
- Owner: bark-simulator
- License: mit
- Created: 2019-04-03T11:36:29.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2024-02-06T20:43:00.000Z (5 months ago)
- Last Synced: 2024-04-28T00:47:34.429Z (2 months ago)
- Topics: 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
- Language: C++
- Homepage: https://bark-simulator.github.io/
- Size: 13.3 MB
- Stars: 274
- Watchers: 17
- Forks: 64
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Lists
- awesome-autonomous-vehicle - BARK - 用于自动驾驶行为规划算法的开发、模拟和基准测试的开源框架 (软件 / 仿真平台)
- awesome-self-driving-cars - BARK - An open-source, semantic simulator built to develop and benchmark novel behavior planners. Runs on multiple platforms and can easily be installed via PyPi. (Simulators)
README
$${\color{red}\text{BARK is not actively developed and maintained any longer.}}$$
$${\color{red}\text{Feel free to fork the repository and continue using BARK under the terms of the MIT license.}}$$
[](https://www.codacy.com/gh/bark-simulator/bark/dashboard?utm_source=github.com&utm_medium=referral&utm_content=bark-simulator/bark&utm_campaign=Badge_Grade)
# BARK - A Tool for **B**ehavior benchm**ARK**ing
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](https://bark-simulator.github.io/tutorials/).
## 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](https://pypi.org/project/bark-simulator/) 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](https://github.com/bark-simulator/bark/tree/master/bark/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](https://github.com/bark-simulator/bark/blob/master/docs/source/installation.md).
To get step-by-step instructions on how to use BARK, you can run our [IPython Notebook tutorials](https://github.com/bark-simulator/bark/tree/master/docs/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](https://bark-simulator.readthedocs.io/en/latest/about.html).
[Example Benchmark](https://github.com/bark-simulator/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](https://arxiv.org/abs/2003.02604) (IROS 2020)
* [Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments](https://arxiv.org/abs/2006.12576) (IV 2020)
* [Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving](https://arxiv.org/abs/2003.11919) (IROS 2020, PLC Workshop)
* [Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning](https://arxiv.org/abs/2009.14186) (IROS 2020, PLC Workshop)
* [Formalizing Traffic Rules for Machine Interpretability](https://arxiv.org/abs/2007.00330) (CAVS 2020)
* [Robust Stochastic Bayesian Games for Behavior Space Coverage](https://arxiv.org/abs/2003.11281) (RSS 2020, Workshop on Interaction and Decision-Making in Autonomous-Driving)
* [Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving](https://arxiv.org/abs/2102.03053) (IV 2021)
* [Risk-Based Safety Envelopes for Autonomous Vehicles Under Perception Uncertainty](https://arxiv.org/abs/2107.09918) (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](https://github.com/bark-simulator/bark-ml/): Machine learning library for decision-making in autonomous driving.
* [BARK-MCTS](https://github.com/bark-simulator/planner-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](https://github.com/bark-simulator/planner-rules-mcts): Integrates traffic rules within Monte Carlo Tree Search with lexicographic ordering.
* [BARK-MIQP](https://github.com/bark-simulator/planner-miqp): MINIVAN Planner based on MIQP for single- and multi-agent planning. Check out the [build instructions](https://github.com/bark-simulator/planner-miqp/blob/master/README.md).
* [BARK-DB](https://github.com/bark-simulator/bark-databasse/): 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](https://github.com/bark-simulator/rule-monitoring): Provides runtime verification of Rules in Linear Temporal Logic (LTL) on simulated BARK traces.
* [CARLA-Interface](https://github.com/bark-simulator/carla-interface): A two-way interface between [CARLA ](https://github.com/carla-simulator/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](https://arxiv.org/abs/2003.02604):
```
@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](https://choosealicense.com/licenses/mit/) License.