{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QHKOKSZ5YYB4TBTFPL3WBDNN6V","short_pith_number":"pith:QHKOKSZ5","schema_version":"1.0","canonical_sha256":"81d4e54b3dc603c986657af7608dadf56bae43b86906c67d00e8c0b125154286","source":{"kind":"arxiv","id":"2412.20784","version":1},"attestation_state":"computed","paper":{"title":"DEMO: A Dynamics-Enhanced Learning Model for Multi-Horizon Trajectory Prediction in Autonomous Vehicles","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chengyue Wang, Guohui Zhang, Haicheng Liao, Kaiqun Zhu, Zhenning Li","submitted_at":"2024-12-30T08:07:21Z","abstract_excerpt":"Autonomous vehicles (AVs) rely on accurate trajectory prediction of surrounding vehicles to ensure the safety of both passengers and other road users. Trajectory prediction spans both short-term and long-term horizons, each requiring distinct considerations: short-term predictions rely on accurately capturing the vehicle's dynamics, while long-term predictions rely on accurately modeling the interaction patterns within the environment. However current approaches, either physics-based or learning-based models, always ignore these distinct considerations, making them struggle to find the optimal"},"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":"2412.20784","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2024-12-30T08:07:21Z","cross_cats_sorted":[],"title_canon_sha256":"82ed67d0d283294c5d17306df885e61fb5e271802b17de6684042560ea1e9cc1","abstract_canon_sha256":"21a2523889d274e16d47a59958a478937bf27ff8ec144802526c1a218ee12d7c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:22.269195Z","signature_b64":"c1q4+GzJwS1ScMWdVxyhEshOmIO/kLrco27SXhnR8Dul3G/4GYBAlatOz/jmAU/Tptw4Y820neAoTRjqAzTkDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81d4e54b3dc603c986657af7608dadf56bae43b86906c67d00e8c0b125154286","last_reissued_at":"2026-07-05T09:55:22.268725Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:22.268725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DEMO: A Dynamics-Enhanced Learning Model for Multi-Horizon Trajectory Prediction in Autonomous Vehicles","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Chengyue Wang, Guohui Zhang, Haicheng Liao, Kaiqun Zhu, Zhenning Li","submitted_at":"2024-12-30T08:07:21Z","abstract_excerpt":"Autonomous vehicles (AVs) rely on accurate trajectory prediction of surrounding vehicles to ensure the safety of both passengers and other road users. Trajectory prediction spans both short-term and long-term horizons, each requiring distinct considerations: short-term predictions rely on accurately capturing the vehicle's dynamics, while long-term predictions rely on accurately modeling the interaction patterns within the environment. However current approaches, either physics-based or learning-based models, always ignore these distinct considerations, making them struggle to find the optimal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20784","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/2412.20784/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":"2412.20784","created_at":"2026-07-05T09:55:22.268787+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.20784v1","created_at":"2026-07-05T09:55:22.268787+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20784","created_at":"2026-07-05T09:55:22.268787+00:00"},{"alias_kind":"pith_short_12","alias_value":"QHKOKSZ5YYB4","created_at":"2026-07-05T09:55:22.268787+00:00"},{"alias_kind":"pith_short_16","alias_value":"QHKOKSZ5YYB4TBTF","created_at":"2026-07-05T09:55:22.268787+00:00"},{"alias_kind":"pith_short_8","alias_value":"QHKOKSZ5","created_at":"2026-07-05T09:55:22.268787+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QHKOKSZ5YYB4TBTFPL3WBDNN6V","json":"https://pith.science/pith/QHKOKSZ5YYB4TBTFPL3WBDNN6V.json","graph_json":"https://pith.science/api/pith-number/QHKOKSZ5YYB4TBTFPL3WBDNN6V/graph.json","events_json":"https://pith.science/api/pith-number/QHKOKSZ5YYB4TBTFPL3WBDNN6V/events.json","paper":"https://pith.science/paper/QHKOKSZ5"},"agent_actions":{"view_html":"https://pith.science/pith/QHKOKSZ5YYB4TBTFPL3WBDNN6V","download_json":"https://pith.science/pith/QHKOKSZ5YYB4TBTFPL3WBDNN6V.json","view_paper":"https://pith.science/paper/QHKOKSZ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.20784&json=true","fetch_graph":"https://pith.science/api/pith-number/QHKOKSZ5YYB4TBTFPL3WBDNN6V/graph.json","fetch_events":"https://pith.science/api/pith-number/QHKOKSZ5YYB4TBTFPL3WBDNN6V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QHKOKSZ5YYB4TBTFPL3WBDNN6V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QHKOKSZ5YYB4TBTFPL3WBDNN6V/action/storage_attestation","attest_author":"https://pith.science/pith/QHKOKSZ5YYB4TBTFPL3WBDNN6V/action/author_attestation","sign_citation":"https://pith.science/pith/QHKOKSZ5YYB4TBTFPL3WBDNN6V/action/citation_signature","submit_replication":"https://pith.science/pith/QHKOKSZ5YYB4TBTFPL3WBDNN6V/action/replication_record"}},"created_at":"2026-07-05T09:55:22.268787+00:00","updated_at":"2026-07-05T09:55:22.268787+00:00"}