{"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/lanegraph2seq-lane-topology-extraction-with","title":"LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement","arxiv_id":"2401.17609","date":"2024-01-31","proceeding":null,"authors":["Renyuan Peng","Xinyue Cai","Hang Xu","Jiachen Lu","Feng Wen","Wei zhang","Li Zhang"],"abstract":"Understanding road structures is crucial for autonomous driving. Intricate road structures are often depicted using lane graphs, which include centerline curves and connections forming a Directed Acyclic Graph (DAG). Accurate extraction of lane graphs relies on precisely estimating vertex and edge information within the DAG. Recent research highlights Transformer-based language models' impressive sequence prediction abilities, making them effective for learning graph representations when graph data are encoded as sequences. However, existing studies focus mainly on modeling vertices explicitly, leaving edge information simply embedded in the network. Consequently, these approaches fall short in the task of lane graph extraction. To address this, we introduce LaneGraph2Seq, a novel approach for lane graph extraction. It leverages a language model with vertex-edge encoding and connectivity enhancement. Our serialization strategy includes a vertex-centric depth-first traversal and a concise edge-based partition sequence. Additionally, we use classifier-free guidance combined with nucleus sampling to improve lane connectivity. We validate our method on prominent datasets, nuScenes and Argoverse 2, showcasing consistent and compelling results. Our LaneGraph2Seq approach demonstrates superior performance compared to state-of-the-art techniques in lane graph extraction.","url_abs":"https://arxiv.org/abs/2401.17609v2","url_pdf":"https://arxiv.org/pdf/2401.17609v2.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":"lanegraph2seq-lane-topology-extraction-with","repo_url":"https://github.com/fudan-zvg/roadnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"lanegraph2seq-lane-topology-extraction-with","repo_url":"https://github.com/fudan-zvg/roadnetworktransformer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2401.17609","atlas_url":"https://app.syntology.ai/?focus=2401.17609","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.17609"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/fudan-zvg/RoadNet","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran_draft_wrong":2,"ran":2},"by_repo_kind":{"official":{"samples":4,"ran":4,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"8945de8524a23486","entry":"cumsum_trick","repo":"fudan-zvg/RoadNet","repo_kind":"official","path":"RoadNetwork-2.0.1/rntr/LiftSplatShoot.py","file_url":"https://github.com/fudan-zvg/RoadNet/blob/HEAD/RoadNetwork-2.0.1/rntr/LiftSplatShoot.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8945de8524a23486"}},{"code_sha256_prefix":"bb7f8ca26bf25aec","entry":"gen_dx_bx","repo":"fudan-zvg/RoadNet","repo_kind":"official","path":"RoadNetwork-2.0.1/rntr/LiftSplatShoot.py","file_url":"https://github.com/fudan-zvg/RoadNet/blob/HEAD/RoadNetwork-2.0.1/rntr/LiftSplatShoot.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bb7f8ca26bf25aec"}},{"code_sha256_prefix":"d7b613aa78ab0d22","entry":"node_match","repo":"fudan-zvg/RoadNet","repo_kind":"official","path":"RoadNetwork-2.0.1/rntr/transforms/roadnet_reach_dist_eval.py","file_url":"https://github.com/fudan-zvg/RoadNet/blob/HEAD/RoadNetwork-2.0.1/rntr/transforms/roadnet_reach_dist_eval.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d7b613aa78ab0d22"}},{"code_sha256_prefix":"718ca707584a0928","entry":"pos2posemb3d","repo":"fudan-zvg/RoadNet","repo_kind":"official","path":"RoadNetwork-2.0.1/rntr/ar_lanegraph2seq_head.py","file_url":"https://github.com/fudan-zvg/RoadNet/blob/HEAD/RoadNetwork-2.0.1/rntr/ar_lanegraph2seq_head.py","link_basis":"plan_row","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"718ca707584a0928"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}