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align=\"center\"\u003e\n\u003cimg src=\"https://github.com/bark-simulator/bark/raw/master/docs/source/bark_logo.jpg\" alt=\"BARK\" /\u003e\n\u003c/p\u003e\n\n$${\\color{red}\\text{BARK is not actively developed and maintained any longer.}}$$\n\n$${\\color{red}\\text{Feel free to fork the repository and continue using BARK under the terms of the MIT license.}}$$\n\n\n![Ubtuntu-CI Build](https://github.com/bark-simulator/bark/workflows/CI/badge.svg)\n![Ubtuntu-ManyLinux Build](https://github.com/bark-simulator/bark/workflows/ManyLinux/badge.svg)\n![NIGHTLY LTL Build](https://github.com/bark-simulator/bark/workflows/NIGHTLY_LTL/badge.svg)\n![CI RSS Build](https://github.com/bark-simulator/bark/workflows/CI_RSS/badge.svg)\n![NIGHTLY Rules MCTS Build](https://github.com/bark-simulator/bark/workflows/NIGHTLY_RULES_MCTS/badge.svg)\n[![Codacy Badge](https://app.codacy.com/project/badge/Grade/b9f484c42194487e9b9b33742381e992)](https://www.codacy.com/gh/bark-simulator/bark/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=bark-simulator/bark\u0026amp;utm_campaign=Badge_Grade)\n# BARK - A Tool for **B**ehavior benchm**ARK**ing\n\nBARK 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/).\n\n## Usage\n\n### (A) Pip Package\n\n*For whom it is: Python evangelists implementing python behavior models or ML scientists using BARK-ML for learning behaviors.*\n\nBark is available as  [PIP-Package](https://pypi.org/project/bark-simulator/) for Ubuntu and MacOS for Python\u003e=3.7. You can install the latest version with \n`pip install bark-simulator`. The Pip package supports full benchmarking functionality of existing behavior models and development of your models within python.\n\nAfter 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. \n\n| Highway Example | Merging Example | Intersection Example |\n| --- | --- | --- |\n| ![Intersection](https://github.com/bark-simulator/bark/raw/master/docs/source/gifs/bark_highway.gif) | ![Intersection](https://github.com/bark-simulator/bark/raw/master/docs/source/gifs/bark_merging.gif) | ![Intersection](https://github.com/bark-simulator/bark/raw/master/docs/source/gifs/bark_intersection.gif) |\n### (B) Build it from Source\n\n*For whom it is: C++ developers creating C++ behavior models, researchers performing benchmarks, or contributors to BARK.*\n\nUse `git clone https://github.com/bark-simulator/bark.git` or download the repository from this page.\nThen follow the instructions at [How to Install BARK](https://github.com/bark-simulator/bark/blob/master/docs/source/installation.md).\n\nTo 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`.\nFor 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).\n\n[Example Benchmark](https://github.com/bark-simulator/example_benchmark) is a running example of how to use BARK for benchmarking for scientific purposes.\n\n## Scientific Publications using BARK\n\n*   [BARK: Open Behavior Benchmarking in Multi-Agent Environments](https://arxiv.org/abs/2003.02604) (IROS 2020)\n*   [Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments](https://arxiv.org/abs/2006.12576) (IV 2020)\n*   [Counterfactual Policy Evaluation for Decision-Making in Autonomous Driving](https://arxiv.org/abs/2003.11919) (IROS 2020,  PLC Workshop)\n*   [Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning](https://arxiv.org/abs/2009.14186) (IROS 2020,  PLC Workshop)\n*   [Formalizing Traffic Rules for Machine Interpretability](https://arxiv.org/abs/2007.00330) (CAVS 2020)\n*   [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)\n*   [Risk-Constrained Interactive Safety under Behavior Uncertainty for Autonomous Driving](https://arxiv.org/abs/2102.03053) (IV 2021)\n*   [Risk-Based Safety Envelopes for Autonomous Vehicles Under Perception Uncertainty](https://arxiv.org/abs/2107.09918) (Arxiv)\n\n## BARK Ecosystem\n\nThe BARK ecosystem is composed of multiple components that all share the common goal to develop and benchmark behavior models:\n\n*   [BARK-ML](https://github.com/bark-simulator/bark-ml/): Machine learning library for decision-making in autonomous driving.\n*   [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.\n*   [BARK-Rules-MCTS](https://github.com/bark-simulator/planner-rules-mcts): Integrates traffic rules within Monte Carlo Tree Search with lexicographic ordering. \n*   [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).\n*   [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. \n*   [BARK-Rule-Monitoring](https://github.com/bark-simulator/rule-monitoring): Provides runtime verification of Rules in Linear Temporal Logic (LTL) on simulated BARK traces.\n*   [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.\n\n## Paper\n\nIf you use BARK, please cite us using the following [paper](https://arxiv.org/abs/2003.02604):\n\n```\n@inproceedings{Bernhard2020,\n    title = {BARK: Open Behavior Benchmarking in Multi-Agent Environments},\n    author = {Bernhard, Julian and Esterle, Klemens and Hart, Patrick and Kessler, Tobias},\n    booktitle = {2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},\n    url = {https://arxiv.org/pdf/2003.02604.pdf},\n    year = {2020}\n}\n```\n\n## Contributing\nPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.\n\nPlease make sure to update tests as appropriate.\n\n## License\n\nBARK specific code is distributed under [MIT](https://choosealicense.com/licenses/mit/) License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbark-simulator%2Fbark","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbark-simulator%2Fbark","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbark-simulator%2Fbark/lists"}