{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:6IYRA5YPTFXLZT4OA75HPGBSMR","short_pith_number":"pith:6IYRA5YP","schema_version":"1.0","canonical_sha256":"f23110770f996ebccf8e07fa7798326468415205855310fcb1a89dead051cd78","source":{"kind":"arxiv","id":"2307.09831","version":1},"attestation_state":"computed","paper":{"title":"A Fast and Map-Free Model for Trajectory Prediction in Traffics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Jingmin Zhang, Junhong Xiang, Zhixiong Nan","submitted_at":"2023-07-19T08:36:31Z","abstract_excerpt":"To handle the two shortcomings of existing methods, (i)nearly all models rely on high-definition (HD) maps, yet the map information is not always available in real traffic scenes and HD map-building is expensive and time-consuming and (ii) existing models usually focus on improving prediction accuracy at the expense of reducing computing efficiency, yet the efficiency is crucial for various real applications, this paper proposes an efficient trajectory prediction model that is not dependent on traffic maps. The core idea of our model is encoding single-agent's spatial-temporal information in t"},"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":"2307.09831","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-07-19T08:36:31Z","cross_cats_sorted":[],"title_canon_sha256":"1a1074bcbae6ea515279eb4c69ba96d257b3c2412aac86799d1f2b7ea35109cb","abstract_canon_sha256":"60994c5325af7c11fa5303f8fb738174cdb5dee2a05ca8c72d5478e6b6d5a011"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:11:35.655500Z","signature_b64":"rO1NAfpWz1Yrb+9iEFnbii5/wtjetZwOhvvWmdHHYWDl0G22Z6nF5ZMPaNijIL/Jp2jY6xtH4Ft4ZIviUQ9pCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f23110770f996ebccf8e07fa7798326468415205855310fcb1a89dead051cd78","last_reissued_at":"2026-07-05T07:11:35.654946Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:11:35.654946Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Fast and Map-Free Model for Trajectory Prediction in Traffics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Jingmin Zhang, Junhong Xiang, Zhixiong Nan","submitted_at":"2023-07-19T08:36:31Z","abstract_excerpt":"To handle the two shortcomings of existing methods, (i)nearly all models rely on high-definition (HD) maps, yet the map information is not always available in real traffic scenes and HD map-building is expensive and time-consuming and (ii) existing models usually focus on improving prediction accuracy at the expense of reducing computing efficiency, yet the efficiency is crucial for various real applications, this paper proposes an efficient trajectory prediction model that is not dependent on traffic maps. The core idea of our model is encoding single-agent's spatial-temporal information in t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.09831","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/2307.09831/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":"2307.09831","created_at":"2026-07-05T07:11:35.655008+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.09831v1","created_at":"2026-07-05T07:11:35.655008+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.09831","created_at":"2026-07-05T07:11:35.655008+00:00"},{"alias_kind":"pith_short_12","alias_value":"6IYRA5YPTFXL","created_at":"2026-07-05T07:11:35.655008+00:00"},{"alias_kind":"pith_short_16","alias_value":"6IYRA5YPTFXLZT4O","created_at":"2026-07-05T07:11:35.655008+00:00"},{"alias_kind":"pith_short_8","alias_value":"6IYRA5YP","created_at":"2026-07-05T07:11:35.655008+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.10961","citing_title":"Map-Free Trajectory Prediction with Map Distillation and Hierarchical Encoding","ref_index":44,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6IYRA5YPTFXLZT4OA75HPGBSMR","json":"https://pith.science/pith/6IYRA5YPTFXLZT4OA75HPGBSMR.json","graph_json":"https://pith.science/api/pith-number/6IYRA5YPTFXLZT4OA75HPGBSMR/graph.json","events_json":"https://pith.science/api/pith-number/6IYRA5YPTFXLZT4OA75HPGBSMR/events.json","paper":"https://pith.science/paper/6IYRA5YP"},"agent_actions":{"view_html":"https://pith.science/pith/6IYRA5YPTFXLZT4OA75HPGBSMR","download_json":"https://pith.science/pith/6IYRA5YPTFXLZT4OA75HPGBSMR.json","view_paper":"https://pith.science/paper/6IYRA5YP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.09831&json=true","fetch_graph":"https://pith.science/api/pith-number/6IYRA5YPTFXLZT4OA75HPGBSMR/graph.json","fetch_events":"https://pith.science/api/pith-number/6IYRA5YPTFXLZT4OA75HPGBSMR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6IYRA5YPTFXLZT4OA75HPGBSMR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6IYRA5YPTFXLZT4OA75HPGBSMR/action/storage_attestation","attest_author":"https://pith.science/pith/6IYRA5YPTFXLZT4OA75HPGBSMR/action/author_attestation","sign_citation":"https://pith.science/pith/6IYRA5YPTFXLZT4OA75HPGBSMR/action/citation_signature","submit_replication":"https://pith.science/pith/6IYRA5YPTFXLZT4OA75HPGBSMR/action/replication_record"}},"created_at":"2026-07-05T07:11:35.655008+00:00","updated_at":"2026-07-05T07:11:35.655008+00:00"}