{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KKQORHMMVJ7QCHFYK4TL5L6OEP","short_pith_number":"pith:KKQORHMM","schema_version":"1.0","canonical_sha256":"52a0e89d8caa7f011cb85726beafce23da2a176fbecd5872bc743ab72ff466dd","source":{"kind":"arxiv","id":"2405.00797","version":1},"attestation_state":"computed","paper":{"title":"ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Chengran Yuan, Jiahui Li, Jiawei Sun, Marcelo H. Ang Jr, Shuo Sun, Tianle Shen, Yuhang Han, Zekai Gu","submitted_at":"2024-05-01T18:16:55Z","abstract_excerpt":"Motion prediction is a challenging problem in autonomous driving as it demands the system to comprehend stochastic dynamics and the multi-modal nature of real-world agent interactions. Diffusion models have recently risen to prominence, and have proven particularly effective in pedestrian motion prediction tasks. However, the significant time consumption and sensitivity to noise have limited the real-time predictive capability of diffusion models. In response to these impediments, we propose a novel diffusion-based, acceleratable framework that adeptly predicts future trajectories of agents wi"},"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":"2405.00797","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2024-05-01T18:16:55Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c6e3fcaf8301663867e53b5fa10bff261282ef5be05b37a6e326faa2617740e6","abstract_canon_sha256":"03c14a85d874194e7518cbf6dbfb08683da33ec5d38daa473cee4067169747f7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:26.371952Z","signature_b64":"lvlIeb/mhfkTJf8nKOS6HnuQOefr7udOyvIOPY+ZQhk4YriwXIu9szjCFpGcIrCNyy/4554iIucMutIqjNgWAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"52a0e89d8caa7f011cb85726beafce23da2a176fbecd5872bc743ab72ff466dd","last_reissued_at":"2026-07-05T08:14:26.371507Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:26.371507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ADM: Accelerated Diffusion Model via Estimated Priors for Robust Motion Prediction under Uncertainties","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Chengran Yuan, Jiahui Li, Jiawei Sun, Marcelo H. Ang Jr, Shuo Sun, Tianle Shen, Yuhang Han, Zekai Gu","submitted_at":"2024-05-01T18:16:55Z","abstract_excerpt":"Motion prediction is a challenging problem in autonomous driving as it demands the system to comprehend stochastic dynamics and the multi-modal nature of real-world agent interactions. Diffusion models have recently risen to prominence, and have proven particularly effective in pedestrian motion prediction tasks. However, the significant time consumption and sensitivity to noise have limited the real-time predictive capability of diffusion models. In response to these impediments, we propose a novel diffusion-based, acceleratable framework that adeptly predicts future trajectories of agents wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.00797","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/2405.00797/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":"2405.00797","created_at":"2026-07-05T08:14:26.371567+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.00797v1","created_at":"2026-07-05T08:14:26.371567+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.00797","created_at":"2026-07-05T08:14:26.371567+00:00"},{"alias_kind":"pith_short_12","alias_value":"KKQORHMMVJ7Q","created_at":"2026-07-05T08:14:26.371567+00:00"},{"alias_kind":"pith_short_16","alias_value":"KKQORHMMVJ7QCHFY","created_at":"2026-07-05T08:14:26.371567+00:00"},{"alias_kind":"pith_short_8","alias_value":"KKQORHMM","created_at":"2026-07-05T08:14:26.371567+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.10465","citing_title":"Image Watermarking of Generative Diffusion Models","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KKQORHMMVJ7QCHFYK4TL5L6OEP","json":"https://pith.science/pith/KKQORHMMVJ7QCHFYK4TL5L6OEP.json","graph_json":"https://pith.science/api/pith-number/KKQORHMMVJ7QCHFYK4TL5L6OEP/graph.json","events_json":"https://pith.science/api/pith-number/KKQORHMMVJ7QCHFYK4TL5L6OEP/events.json","paper":"https://pith.science/paper/KKQORHMM"},"agent_actions":{"view_html":"https://pith.science/pith/KKQORHMMVJ7QCHFYK4TL5L6OEP","download_json":"https://pith.science/pith/KKQORHMMVJ7QCHFYK4TL5L6OEP.json","view_paper":"https://pith.science/paper/KKQORHMM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.00797&json=true","fetch_graph":"https://pith.science/api/pith-number/KKQORHMMVJ7QCHFYK4TL5L6OEP/graph.json","fetch_events":"https://pith.science/api/pith-number/KKQORHMMVJ7QCHFYK4TL5L6OEP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KKQORHMMVJ7QCHFYK4TL5L6OEP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KKQORHMMVJ7QCHFYK4TL5L6OEP/action/storage_attestation","attest_author":"https://pith.science/pith/KKQORHMMVJ7QCHFYK4TL5L6OEP/action/author_attestation","sign_citation":"https://pith.science/pith/KKQORHMMVJ7QCHFYK4TL5L6OEP/action/citation_signature","submit_replication":"https://pith.science/pith/KKQORHMMVJ7QCHFYK4TL5L6OEP/action/replication_record"}},"created_at":"2026-07-05T08:14:26.371567+00:00","updated_at":"2026-07-05T08:14:26.371567+00:00"}