{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:AQWS2HYM2ZORUSA3RTKQR632SI","short_pith_number":"pith:AQWS2HYM","schema_version":"1.0","canonical_sha256":"042d2d1f0cd65d1a481b8cd508fb7a9221bac558d8fe81aa3448df61b7ddd761","source":{"kind":"arxiv","id":"2108.08236","version":3},"attestation_state":"computed","paper":{"title":"LOKI: Long Term and Key Intentions for Trajectory Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MA","cs.RO"],"primary_cat":"cs.CV","authors_text":"Akira Kanehara, Chiho Choi, Haiming Gang, Harshayu Girase, Jiachen Li, Karttikeya Mangalam, Srikanth Malla","submitted_at":"2021-08-18T16:57:03Z","abstract_excerpt":"Recent advances in trajectory prediction have shown that explicit reasoning about agents' intent is important to accurately forecast their motion. However, the current research activities are not directly applicable to intelligent and safety critical systems. This is mainly because very few public datasets are available, and they only consider pedestrian-specific intents for a short temporal horizon from a restricted egocentric view. To this end, we propose LOKI (LOng term and Key Intentions), a novel large-scale dataset that is designed to tackle joint trajectory and intention prediction for "},"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":"2108.08236","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-08-18T16:57:03Z","cross_cats_sorted":["cs.AI","cs.LG","cs.MA","cs.RO"],"title_canon_sha256":"b2cc5710dcaddd5709a8b7ee9bcd56c444ad379804dd56bc236ff1f277d6712d","abstract_canon_sha256":"5c36ddb30f3a053f4dfd2ec32cbbbffcbebc2ded6a38e54d21a6021c38ddb91b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:15:11.517853Z","signature_b64":"4ukrhAizZvvf8OO1tWkBA/fXopjxGU7aT1zDHML5ZesNGhzDarOaY6o3uGg+oXhzth2FRV/lH3TouNA+9CC2Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"042d2d1f0cd65d1a481b8cd508fb7a9221bac558d8fe81aa3448df61b7ddd761","last_reissued_at":"2026-07-05T03:15:11.517426Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:15:11.517426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LOKI: Long Term and Key Intentions for Trajectory Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.MA","cs.RO"],"primary_cat":"cs.CV","authors_text":"Akira Kanehara, Chiho Choi, Haiming Gang, Harshayu Girase, Jiachen Li, Karttikeya Mangalam, Srikanth Malla","submitted_at":"2021-08-18T16:57:03Z","abstract_excerpt":"Recent advances in trajectory prediction have shown that explicit reasoning about agents' intent is important to accurately forecast their motion. However, the current research activities are not directly applicable to intelligent and safety critical systems. This is mainly because very few public datasets are available, and they only consider pedestrian-specific intents for a short temporal horizon from a restricted egocentric view. To this end, we propose LOKI (LOng term and Key Intentions), a novel large-scale dataset that is designed to tackle joint trajectory and intention prediction for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.08236","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/2108.08236/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":"2108.08236","created_at":"2026-07-05T03:15:11.517489+00:00"},{"alias_kind":"arxiv_version","alias_value":"2108.08236v3","created_at":"2026-07-05T03:15:11.517489+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.08236","created_at":"2026-07-05T03:15:11.517489+00:00"},{"alias_kind":"pith_short_12","alias_value":"AQWS2HYM2ZOR","created_at":"2026-07-05T03:15:11.517489+00:00"},{"alias_kind":"pith_short_16","alias_value":"AQWS2HYM2ZORUSA3","created_at":"2026-07-05T03:15:11.517489+00:00"},{"alias_kind":"pith_short_8","alias_value":"AQWS2HYM","created_at":"2026-07-05T03:15:11.517489+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.14727","citing_title":"Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AQWS2HYM2ZORUSA3RTKQR632SI","json":"https://pith.science/pith/AQWS2HYM2ZORUSA3RTKQR632SI.json","graph_json":"https://pith.science/api/pith-number/AQWS2HYM2ZORUSA3RTKQR632SI/graph.json","events_json":"https://pith.science/api/pith-number/AQWS2HYM2ZORUSA3RTKQR632SI/events.json","paper":"https://pith.science/paper/AQWS2HYM"},"agent_actions":{"view_html":"https://pith.science/pith/AQWS2HYM2ZORUSA3RTKQR632SI","download_json":"https://pith.science/pith/AQWS2HYM2ZORUSA3RTKQR632SI.json","view_paper":"https://pith.science/paper/AQWS2HYM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2108.08236&json=true","fetch_graph":"https://pith.science/api/pith-number/AQWS2HYM2ZORUSA3RTKQR632SI/graph.json","fetch_events":"https://pith.science/api/pith-number/AQWS2HYM2ZORUSA3RTKQR632SI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AQWS2HYM2ZORUSA3RTKQR632SI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AQWS2HYM2ZORUSA3RTKQR632SI/action/storage_attestation","attest_author":"https://pith.science/pith/AQWS2HYM2ZORUSA3RTKQR632SI/action/author_attestation","sign_citation":"https://pith.science/pith/AQWS2HYM2ZORUSA3RTKQR632SI/action/citation_signature","submit_replication":"https://pith.science/pith/AQWS2HYM2ZORUSA3RTKQR632SI/action/replication_record"}},"created_at":"2026-07-05T03:15:11.517489+00:00","updated_at":"2026-07-05T03:15:11.517489+00:00"}