{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/dataset/tusimple/papers/ran/1","list_of":"/dataset/tusimple","dataset":"TuSimple","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","samples_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,9],"of":9,"counts":{"papers_with_a_benchmark_row":25,"with_a_code_link":18,"where_syntology_ran_a_sample":9,"not_listed_spam_title":0,"listed":25,"listed_where_code_ran":9,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":9,"every_run_a_failure_of_syntologys_instrument":0,"listed_with_a_run_with_no_instrument_failure":9,"listed_every_run_a_failure_of_syntologys_instrument":0,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/tusimple/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/a-keypoint-based-global-association-network","slug":"a-keypoint-based-global-association-network","title":"A Keypoint-based Global Association Network for Lane Detection","date":"2022-04-15","arxiv_id":"2204.07335","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":6,"samples_constructed":3,"samples_ran_checked":6,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["wolfwjs/ganet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/a-keypoint-based-global-association-network#ran","syntology_url":"https://syntology.ai/paper/2204.07335","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.07335"}}}},{"paper":"/paper/eigenlanes-data-driven-lane-descriptors-for","slug":"eigenlanes-data-driven-lane-descriptors-for","title":"Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes","date":"2022-03-29","arxiv_id":"2203.15302","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":15,"samples_ran":10,"samples_constructed":0,"samples_ran_checked":10,"samples_ran_instrument_failed":0,"samples_unverified":5,"pointer_only_for_licence":0,"official":{"repos":["dongkwonjin/eigenlanes"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/eigenlanes-data-driven-lane-descriptors-for#ran","syntology_url":"https://syntology.ai/paper/2203.15302","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15302"}}}},{"paper":"/paper/clrnet-cross-layer-refinement-network-for","slug":"clrnet-cross-layer-refinement-network-for","title":"CLRNet: Cross Layer Refinement Network for Lane Detection","date":"2022-03-19","arxiv_id":"2203.10350","rows_on_this_dataset":3,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["Turoad/clrnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/clrnet-cross-layer-refinement-network-for#ran","syntology_url":"https://syntology.ai/paper/2203.10350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10350"}}}},{"paper":"/paper/rethinking-efficient-lane-detection-via-curve","slug":"rethinking-efficient-lane-detection-via-curve","title":"Rethinking Efficient Lane Detection via Curve Modeling","date":"2022-03-04","arxiv_id":"2203.02431","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":12,"samples_ran":12,"samples_constructed":0,"samples_ran_checked":12,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["voldemortX/pytorch-auto-drive"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/rethinking-efficient-lane-detection-via-curve#ran","syntology_url":"https://syntology.ai/paper/2203.02431","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02431"}}}},{"paper":"/paper/condlanenet-a-top-to-down-lane-detection","slug":"condlanenet-a-top-to-down-lane-detection","title":"CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution","date":"2021-05-11","arxiv_id":"2105.05003","rows_on_this_dataset":4,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["aliyun/conditional-lane-detection"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/condlanenet-a-top-to-down-lane-detection#ran","syntology_url":"https://syntology.ai/paper/2105.05003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.05003"}}}},{"paper":"/paper/keep-your-eyes-on-the-lane-attention-guided","slug":"keep-your-eyes-on-the-lane-attention-guided","title":"Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection","date":"2020-10-22","arxiv_id":"2010.12035","rows_on_this_dataset":3,"code_links":3,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":4,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["lucastabelini/LaneATT"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/keep-your-eyes-on-the-lane-attention-guided#ran","syntology_url":"https://syntology.ai/paper/2010.12035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2010.12035"}}}},{"paper":"/paper/polylanenet-lane-estimation-via-deep","slug":"polylanenet-lane-estimation-via-deep","title":"PolyLaneNet: Lane Estimation via Deep Polynomial Regression","date":"2020-04-23","arxiv_id":"2004.10924","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":10,"samples_ran":9,"samples_constructed":0,"samples_ran_checked":9,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["lucastabelini/PolyLaneNet"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/polylanenet-lane-estimation-via-deep#ran","syntology_url":"https://syntology.ai/paper/2004.10924","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.10924"}}}},{"paper":"/paper/learning-lightweight-lane-detection-cnns-by","slug":"learning-lightweight-lane-detection-cnns-by","title":"Learning Lightweight Lane Detection CNNs by Self Attention Distillation","date":"2019-08-02","arxiv_id":"1908.00821","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":8,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":5,"samples_ran_instrument_failed":2,"samples_unverified":1,"pointer_only_for_licence":1,"official":{"repos":["cardwing/Codes-for-Lane-Detection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/learning-lightweight-lane-detection-cnns-by#ran","syntology_url":"https://syntology.ai/paper/1908.00821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.00821"}}}},{"paper":"/paper/towards-end-to-end-lane-detection-an-instance","slug":"towards-end-to-end-lane-detection-an-instance","title":"Towards End-to-End Lane Detection: an Instance Segmentation Approach","date":"2018-02-15","arxiv_id":"1802.05591","rows_on_this_dataset":1,"code_links":22,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":34,"samples_ran":16,"samples_constructed":0,"samples_ran_checked":16,"samples_ran_instrument_failed":0,"samples_unverified":18,"pointer_only_for_licence":2,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/towards-end-to-end-lane-detection-an-instance#ran","syntology_url":"https://syntology.ai/paper/1802.05591","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.05591"}}}}],"record_sha256":"84a0b384a3f7aa95e4b3331405fbb9634ce589c65264d4b50f19bb53f9f73eae","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}