{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:272UHE2RMN7WNEJVZFUY7FNTL5","short_pith_number":"pith:272UHE2R","schema_version":"1.0","canonical_sha256":"d7f5439351637f669135c9698f95b35f459a83085e2d088b0daf88a25297e0bd","source":{"kind":"arxiv","id":"2310.12007","version":3},"attestation_state":"computed","paper":{"title":"KI-PMF: Knowledge Integrated Plausible Motion Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Abhishek Vivekanandan, Ahmed Abouelazm, J. Marius Z\\\"ollner, Philip Sch\\\"orner","submitted_at":"2023-10-18T14:40:52Z","abstract_excerpt":"Accurately forecasting the motion of traffic actors is crucial for the deployment of autonomous vehicles at a large scale. Current trajectory forecasting approaches primarily concentrate on optimizing a loss function with a specific metric, which can result in predictions that do not adhere to physical laws or violate external constraints. Our objective is to incorporate explicit knowledge priors that allow a network to forecast future trajectories in compliance with both the kinematic constraints of a vehicle and the geometry of the driving environment. To achieve this, we introduce a non-par"},"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":"2310.12007","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-10-18T14:40:52Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"e4eb7d093cbccb93f0d1a5688609e639e149b7f0f8d5e108d8023b2aff698885","abstract_canon_sha256":"abf920f7f3a1d6e0d1dab600ba06c24224ce802846f3e01830b85b77c1fa8888"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:50:08.066093Z","signature_b64":"Dyb+rU3errQOHtExuIYI+v3nJX/Y+Lq1gVof/KHpg3b/LuoR9hQ9cnZJls7bfdHIOGXsFUYgz/mkXNY+bXR3Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7f5439351637f669135c9698f95b35f459a83085e2d088b0daf88a25297e0bd","last_reissued_at":"2026-07-05T08:50:08.065687Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:50:08.065687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KI-PMF: Knowledge Integrated Plausible Motion Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.RO","authors_text":"Abhishek Vivekanandan, Ahmed Abouelazm, J. Marius Z\\\"ollner, Philip Sch\\\"orner","submitted_at":"2023-10-18T14:40:52Z","abstract_excerpt":"Accurately forecasting the motion of traffic actors is crucial for the deployment of autonomous vehicles at a large scale. Current trajectory forecasting approaches primarily concentrate on optimizing a loss function with a specific metric, which can result in predictions that do not adhere to physical laws or violate external constraints. Our objective is to incorporate explicit knowledge priors that allow a network to forecast future trajectories in compliance with both the kinematic constraints of a vehicle and the geometry of the driving environment. To achieve this, we introduce a non-par"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.12007","kind":"arxiv","version":3},"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/2310.12007/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":"2310.12007","created_at":"2026-07-05T08:50:08.065740+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.12007v3","created_at":"2026-07-05T08:50:08.065740+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.12007","created_at":"2026-07-05T08:50:08.065740+00:00"},{"alias_kind":"pith_short_12","alias_value":"272UHE2RMN7W","created_at":"2026-07-05T08:50:08.065740+00:00"},{"alias_kind":"pith_short_16","alias_value":"272UHE2RMN7WNEJV","created_at":"2026-07-05T08:50:08.065740+00:00"},{"alias_kind":"pith_short_8","alias_value":"272UHE2R","created_at":"2026-07-05T08:50:08.065740+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02571","citing_title":"Contrast & Compress: Learning Lightweight Embeddings for Short Trajectories","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/272UHE2RMN7WNEJVZFUY7FNTL5","json":"https://pith.science/pith/272UHE2RMN7WNEJVZFUY7FNTL5.json","graph_json":"https://pith.science/api/pith-number/272UHE2RMN7WNEJVZFUY7FNTL5/graph.json","events_json":"https://pith.science/api/pith-number/272UHE2RMN7WNEJVZFUY7FNTL5/events.json","paper":"https://pith.science/paper/272UHE2R"},"agent_actions":{"view_html":"https://pith.science/pith/272UHE2RMN7WNEJVZFUY7FNTL5","download_json":"https://pith.science/pith/272UHE2RMN7WNEJVZFUY7FNTL5.json","view_paper":"https://pith.science/paper/272UHE2R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.12007&json=true","fetch_graph":"https://pith.science/api/pith-number/272UHE2RMN7WNEJVZFUY7FNTL5/graph.json","fetch_events":"https://pith.science/api/pith-number/272UHE2RMN7WNEJVZFUY7FNTL5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/272UHE2RMN7WNEJVZFUY7FNTL5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/272UHE2RMN7WNEJVZFUY7FNTL5/action/storage_attestation","attest_author":"https://pith.science/pith/272UHE2RMN7WNEJVZFUY7FNTL5/action/author_attestation","sign_citation":"https://pith.science/pith/272UHE2RMN7WNEJVZFUY7FNTL5/action/citation_signature","submit_replication":"https://pith.science/pith/272UHE2RMN7WNEJVZFUY7FNTL5/action/replication_record"}},"created_at":"2026-07-05T08:50:08.065740+00:00","updated_at":"2026-07-05T08:50:08.065740+00:00"}