{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:O5K5LSSLWF65LHWWP73SECBTSP","short_pith_number":"pith:O5K5LSSL","schema_version":"1.0","canonical_sha256":"7755d5ca4bb17dd59ed67ff722083393c03f9ad9f344dab38fd5ab6ba2b0c7b8","source":{"kind":"arxiv","id":"2406.10125","version":1},"attestation_state":"computed","paper":{"title":"MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Shen, Jichao Jiao, Jinluo Xie, Mai Liu, Pengfei Xu, Runbo Hu, Tengfei Xing, Wei Shao, Yueming Zhang, Zhongyu Yang","submitted_at":"2024-06-14T15:31:45Z","abstract_excerpt":"Autonomous driving without high-definition (HD) maps demands a higher level of active scene understanding. In this competition, the organizers provided the multi-perspective camera images and standard-definition (SD) maps to explore the boundaries of scene reasoning capabilities. We found that most existing algorithms construct Bird's Eye View (BEV) features from these multi-perspective images and use multi-task heads to delineate road centerlines, boundary lines, pedestrian crossings, and other areas. However, these algorithms perform poorly at the far end of roads and struggle when the prima"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2406.10125","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-06-14T15:31:45Z","cross_cats_sorted":[],"title_canon_sha256":"0838382b1ba5ebbddc941a5d5b3727f4ca732f4f3caff23797b7b7dbadd0e976","abstract_canon_sha256":"279f34f2109d1fca60d9b1d5fcc54fe1e1d92a9e84dd86fc1ab8525167109d3b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:05.715956Z","signature_b64":"jy+f+saGT814MRi/xQZJ3bNR81Gb6wGHcqAoIRNjgo+c2O7WssR7ajy7mJEqvFzltNUU5xal+1TfntoK+zP5Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7755d5ca4bb17dd59ed67ff722083393c03f9ad9f344dab38fd5ab6ba2b0c7b8","last_reissued_at":"2026-07-05T08:32:05.715448Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:05.715448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Shen, Jichao Jiao, Jinluo Xie, Mai Liu, Pengfei Xu, Runbo Hu, Tengfei Xing, Wei Shao, Yueming Zhang, Zhongyu Yang","submitted_at":"2024-06-14T15:31:45Z","abstract_excerpt":"Autonomous driving without high-definition (HD) maps demands a higher level of active scene understanding. In this competition, the organizers provided the multi-perspective camera images and standard-definition (SD) maps to explore the boundaries of scene reasoning capabilities. We found that most existing algorithms construct Bird's Eye View (BEV) features from these multi-perspective images and use multi-task heads to delineate road centerlines, boundary lines, pedestrian crossings, and other areas. However, these algorithms perform poorly at the far end of roads and struggle when the prima"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.10125","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.10125/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2406.10125","created_at":"2026-07-05T08:32:05.715506+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.10125v1","created_at":"2026-07-05T08:32:05.715506+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.10125","created_at":"2026-07-05T08:32:05.715506+00:00"},{"alias_kind":"pith_short_12","alias_value":"O5K5LSSLWF65","created_at":"2026-07-05T08:32:05.715506+00:00"},{"alias_kind":"pith_short_16","alias_value":"O5K5LSSLWF65LHWW","created_at":"2026-07-05T08:32:05.715506+00:00"},{"alias_kind":"pith_short_8","alias_value":"O5K5LSSL","created_at":"2026-07-05T08:32:05.715506+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.01397","citing_title":"Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps","ref_index":55,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O5K5LSSLWF65LHWWP73SECBTSP","json":"https://pith.science/pith/O5K5LSSLWF65LHWWP73SECBTSP.json","graph_json":"https://pith.science/api/pith-number/O5K5LSSLWF65LHWWP73SECBTSP/graph.json","events_json":"https://pith.science/api/pith-number/O5K5LSSLWF65LHWWP73SECBTSP/events.json","paper":"https://pith.science/paper/O5K5LSSL"},"agent_actions":{"view_html":"https://pith.science/pith/O5K5LSSLWF65LHWWP73SECBTSP","download_json":"https://pith.science/pith/O5K5LSSLWF65LHWWP73SECBTSP.json","view_paper":"https://pith.science/paper/O5K5LSSL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.10125&json=true","fetch_graph":"https://pith.science/api/pith-number/O5K5LSSLWF65LHWWP73SECBTSP/graph.json","fetch_events":"https://pith.science/api/pith-number/O5K5LSSLWF65LHWWP73SECBTSP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O5K5LSSLWF65LHWWP73SECBTSP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O5K5LSSLWF65LHWWP73SECBTSP/action/storage_attestation","attest_author":"https://pith.science/pith/O5K5LSSLWF65LHWWP73SECBTSP/action/author_attestation","sign_citation":"https://pith.science/pith/O5K5LSSLWF65LHWWP73SECBTSP/action/citation_signature","submit_replication":"https://pith.science/pith/O5K5LSSLWF65LHWWP73SECBTSP/action/replication_record"}},"created_at":"2026-07-05T08:32:05.715506+00:00","updated_at":"2026-07-05T08:32:05.715506+00:00"}