{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-a-lattice-planner-control-set-for","title":"Learning a Lattice Planner Control Set for Autonomous Vehicles","arxiv_id":"1903.02044","date":"2019-03-05","proceeding":null,"authors":["Ryan De Iaco","Stephen L. Smith","Krzysztof Czarnecki"],"abstract":"This paper introduces a method to compute a sparse lattice planner control\nset that is suited to a particular task by learning from a representative\ndataset of vehicle paths. To do this, we use a scoring measure similar to the\nFr\\'echet distance and propose an algorithm for evaluating a given control set\naccording to the scoring measure. Control actions are then selected from a\ndense control set according to an objective function that rewards improvements\nin matching the dataset while also encouraging sparsity. This method is\nevaluated across several experiments involving real and synthetic datasets, and\nit is shown to generate smaller control sets when compared to the previous\nstate-of-the-art lattice control set computation technique, with these smaller\ncontrol sets maintaining a high degree of manoeuvrability in the required task.\nThis results in a planning time speedup of up to 4.31x when using the learned\ncontrol set over the state-of-the-art computed control set. In addition, we\nshow the learned control sets are better able to capture the driving style of\nthe dataset in terms of path curvature.","url_abs":"http://arxiv.org/abs/1903.02044v2","url_pdf":"http://arxiv.org/pdf/1903.02044v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-a-lattice-planner-control-set-for","repo_url":"https://github.com/rdeiaco/learning_lattice_planner","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"autonomous-vehicles","task_name":"Autonomous Vehicles"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}