{"id":13445580,"url":"https://github.com/TUMFTM/GraphBasedLocalTrajectoryPlanner","last_synced_at":"2025-03-20T21:30:57.058Z","repository":{"id":37628821,"uuid":"264136870","full_name":"TUMFTM/GraphBasedLocalTrajectoryPlanner","owner":"TUMFTM","description":"Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.","archived":false,"fork":false,"pushed_at":"2023-07-06T21:54:20.000Z","size":13083,"stargazers_count":234,"open_issues_count":3,"forks_count":54,"subscribers_count":7,"default_branch":"master","last_synced_at":"2024-08-01T05:15:32.281Z","etag":null,"topics":["graph","planner"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"lgpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/TUMFTM.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}},"created_at":"2020-05-15T08:16:44.000Z","updated_at":"2024-07-02T10:57:33.000Z","dependencies_parsed_at":"2022-09-06T09:02:09.285Z","dependency_job_id":null,"html_url":"https://github.com/TUMFTM/GraphBasedLocalTrajectoryPlanner","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TUMFTM%2FGraphBasedLocalTrajectoryPlanner","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TUMFTM%2FGraphBasedLocalTrajectoryPlanner/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TUMFTM%2FGraphBasedLocalTrajectoryPlanner/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/TUMFTM%2FGraphBasedLocalTrajectoryPlanner/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/TUMFTM","download_url":"https://codeload.github.com/TUMFTM/GraphBasedLocalTrajectoryPlanner/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":221807738,"owners_count":16883639,"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","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":["graph","planner"],"created_at":"2024-07-31T05:00:36.150Z","updated_at":"2025-03-20T21:30:57.052Z","avatar_url":"https://github.com/TUMFTM.png","language":"Python","funding_links":[],"categories":["6. Planning","Planning and Control"],"sub_categories":["3.4 High Performance Inference","Vector Map"],"readme":"# Graph-Based Local Trajectory Planner\n\n![Title Picture Local Planner](docs/source/figures/Title.png)\n\nThe graph-based local trajectory planner is python-based and comes with open interfaces as well as debug, visualization\nand development tools. The local planner is designed in a way to return an action set (e.g. keep straight, pass left,\npass right), where each action is the globally cost optimal solution for that task. If any of the action primitives is\nnot feasible, it is not returned in the set. That way, one can either select available actions based on a priority list\n(e.g. try to pass if possible) or use an own dedicated behaviour planner.\n\nThe planner was used on a real race vehicle during the Roborace Season Alpha and achieved speeds above 200kph.\nA video of the performance at the Monteblanco track can be found [here](https://www.youtube.com/watch?v=-vqQBuTQhQw).\n\n### Disclaimer\nThis software is provided *as-is* and has not been subject to a certified safety validation. Autonomous Driving is a\nhighly complex and dangerous task. In case you plan to use this software on a vehicle, it is by all means required that\nyou assess the overall safety of your project as a whole. By no means is this software a replacement for a valid \nsafety-concept. See the license for more details.\n\n\n### Documentation\nThe documentation of the project can be found [here](https://graphbasedlocaltrajectoryplanner.readthedocs.io/).\n\n\n### Contributions\n[1] T. Stahl, A. Wischnewski, J. Betz, and M. Lienkamp,\n“Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios,”\nin 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Oct. 2019, pp. 3149–3154.\\\n[(view pre-print)](https://arxiv.org/pdf/2005.08664\u003e`)\n\nContact: [Tim Stahl](mailto:stahl@ftm.mw.tum.de).\n\nIf you find our work useful in your research, please consider citing: \n\n```\n   @inproceedings{stahl2019,\n     title = {Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios},\n     booktitle = {2019 IEEE Intelligent Transportation Systems Conference (ITSC)},\n     author = {Stahl, Tim and Wischnewski, Alexander and Betz, Johannes and Lienkamp, Markus},\n     year = {2019},\n     pages = {3149--3154}\n   }\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTUMFTM%2FGraphBasedLocalTrajectoryPlanner","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FTUMFTM%2FGraphBasedLocalTrajectoryPlanner","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FTUMFTM%2FGraphBasedLocalTrajectoryPlanner/lists"}