{"id":19756076,"url":"https://github.com/luistar/genrl-lkas-testing-tool","last_synced_at":"2026-06-17T16:36:26.547Z","repository":{"id":119218846,"uuid":"440437994","full_name":"luistar/GenRL-lkas-testing-tool","owner":"luistar","description":"Generate effective tests for self-driving cars using Deep Reinforcement Learning","archived":false,"fork":false,"pushed_at":"2022-03-03T17:52:01.000Z","size":37424,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-26T12:40:01.721Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/luistar.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-12-21T08:06:26.000Z","updated_at":"2025-06-30T17:39:37.000Z","dependencies_parsed_at":"2023-04-30T08:26:15.379Z","dependency_job_id":null,"html_url":"https://github.com/luistar/GenRL-lkas-testing-tool","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/luistar/GenRL-lkas-testing-tool","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/luistar%2FGenRL-lkas-testing-tool","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/luistar%2FGenRL-lkas-testing-tool/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/luistar%2FGenRL-lkas-testing-tool/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/luistar%2FGenRL-lkas-testing-tool/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/luistar","download_url":"https://codeload.github.com/luistar/GenRL-lkas-testing-tool/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/luistar%2FGenRL-lkas-testing-tool/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":281068968,"owners_count":26438554,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-26T02:00:06.575Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-12T03:14:39.039Z","updated_at":"2025-10-26T06:40:24.048Z","avatar_url":"https://github.com/luistar.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# GenRL at the Cyber-Physical Systems Testing Competition #\n\nGenRL is a tool that **Gen**erates effective test cases for a lane-keeping system in a simulated\nenvironment using **R**einforcement **L**earning (RL).\n\nThis repository is a fork of [tool-competition-av](https://github.com/se2p/tool-competition-av), in which we implemented our RL-based approach.\n\nInstall additional dependencies for GenRL with\n```\npip3 install torch==1.10.1+cu113 torchvision==0.11.2+cu113 torchaudio===0.10.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html\n\npip install -r additional-requirements.txt \n```\n\nTo start the test generator using the tool competition pipeline, run `competition.py` with the following command line parameters:\n```\n--module-name genrl_sbst2022.genrl_test_generator --class-name GenrlTestGenerator \n```\n\n# Cyber-Physical Systems Testing Competition #\n\nThe [SBST Workshop](https://sbst22.github.io/) offers a challenge for software testers who want to work with self-driving cars in the context of the usual [tool competition](https://sbst22.github.io/tools/).\n\n## Important Dates\n\nThe deadline to submit your tool is: **January 14th 2022**\n\nThe results of the evaluation will be communicated to participants on: **February 25th 2022**\n\nThe camera-ready paper describing your tool is due to: **Sunday March 18th 2020**\n\n## Goal ##\nThe competitors should generate virtual roads to test a lane keeping assist system using the provided code_pipeline.\n\nThe generated roads are evaluated in a driving simulator. We partnered with BeamNG GmbH which offers a version of their simulators for researchers, named [BeamNG.tech](https://www.beamng.tech/). This simulator is ideal for researchers due to its state-of-the-art soft-body physics simulation, ease of access to sensory data, and a Python API to control the simulation.\n\n[![Video by BeamNg GmbH](https://github.com/BeamNG/BeamNGpy/raw/master/media/steering.gif)](https://github.com/BeamNG/BeamNGpy/raw/master/media/steering.gif)\n\n## Implement Your Test Generator ##\nWe make available a [code pipeline](code_pipeline) that will integrate your test generator with the simulator by validating, executing and evaluating your test cases. Moreover, we offer some [sample test generators](sample_test_generators/README.md) to show how to use our code pipeline.\n\n## Information About the Competition ##\nMore information can be found on the SBST tool competition website: [https://sbst22.github.io/tools/](https://sbst22.github.io/tools/)\n\n## Repository Structure ##\n[Code pipeline](code_pipeline): code that integrates your test generator with the simulator\n\n[Self driving car testing library](self_driving): library that helps the integration of the test input generators, our code pipeline, and the BeamNG simulator\n\n[Scenario template](levels_template/tig): basic scenario used in this competition\n\n[Documentation](documentation/README.md): contains the installation guide, detailed rules of the competition, and the frequently asked questions\n\n[Sample test generators](sample_test_generators/README.md): sample test generators already integrated with the code pipeline for illustrative purposes \n\n[Requirements](requirements.txt): contains the list of the required packages.\n\n\n## License ##\nThe software we developed is distributed under GNU GPL license. See the [LICENSE.md](LICENSE.md) file.\n\n## Contacts ##\n\nDr. Alessio Gambi  - Passau University, Germany - alessio.gambi@uni-passau.de\n\nDr. Vincenzo Riccio  - Universita' della Svizzera Italiana, Lugano, Switzerland - vincenzo.riccio@usi.ch\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fluistar%2Fgenrl-lkas-testing-tool","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fluistar%2Fgenrl-lkas-testing-tool","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fluistar%2Fgenrl-lkas-testing-tool/lists"}