{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KRGKATKN3PGCHCFBCYOZEXSTI3","short_pith_number":"pith:KRGKATKN","schema_version":"1.0","canonical_sha256":"544ca04d4ddbcc2388a1161d925e5346e841aa5e2ffb63e7f79a0fb8cb1d7f73","source":{"kind":"arxiv","id":"2606.26661","version":1},"attestation_state":"computed","paper":{"title":"LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Changhyun Choi, H. Jin Kim, Hoseong Jung, Jeongtae Her, Sangjin Han","submitted_at":"2026-06-25T06:52:17Z","abstract_excerpt":"Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they often overlook the adherence to lane topology of multimodal predictions, particularly for lower-probability modes. Consequently, predicted trajectories may violate physical and logical constraints, making the prediction set unreliable for safety-critical planning. In this paper, we propose LAMP (Lane-Aligned Motion Primitives), a topology-aware forecasting framework that anchors "},"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":"2606.26661","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-25T06:52:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"944ec779d425436da1479e5a3bfd43042f32725de9e257fb3913616ddfd69c2f","abstract_canon_sha256":"143cad1b3521a1cc72fed0023d114034ebf0c7424ec25a2362d04fb6b2df4870"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:15:56.159325Z","signature_b64":"pNLLYECyywjt38GrCBBru5+kVDUzMhwYFQxHUbFKVBLcglbscEPaFEnWM0w48cdBSztorNGrnPcXVxlZ/LrsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"544ca04d4ddbcc2388a1161d925e5346e841aa5e2ffb63e7f79a0fb8cb1d7f73","last_reissued_at":"2026-06-26T01:15:56.158716Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:15:56.158716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LAMP: Lane-Aligned Motion Primitives for Feasible Trajectory Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Changhyun Choi, H. Jin Kim, Hoseong Jung, Jeongtae Her, Sangjin Han","submitted_at":"2026-06-25T06:52:17Z","abstract_excerpt":"Motion forecasting is essential for autonomous driving systems to enable safe decision-making and planning in complex driving scenarios. While existing predictors excel at minimizing standard displacement errors, they often overlook the adherence to lane topology of multimodal predictions, particularly for lower-probability modes. Consequently, predicted trajectories may violate physical and logical constraints, making the prediction set unreliable for safety-critical planning. In this paper, we propose LAMP (Lane-Aligned Motion Primitives), a topology-aware forecasting framework that anchors "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26661","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/2606.26661/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":"2606.26661","created_at":"2026-06-26T01:15:56.158782+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.26661v1","created_at":"2026-06-26T01:15:56.158782+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26661","created_at":"2026-06-26T01:15:56.158782+00:00"},{"alias_kind":"pith_short_12","alias_value":"KRGKATKN3PGC","created_at":"2026-06-26T01:15:56.158782+00:00"},{"alias_kind":"pith_short_16","alias_value":"KRGKATKN3PGCHCFB","created_at":"2026-06-26T01:15:56.158782+00:00"},{"alias_kind":"pith_short_8","alias_value":"KRGKATKN","created_at":"2026-06-26T01:15:56.158782+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/KRGKATKN3PGCHCFBCYOZEXSTI3","json":"https://pith.science/pith/KRGKATKN3PGCHCFBCYOZEXSTI3.json","graph_json":"https://pith.science/api/pith-number/KRGKATKN3PGCHCFBCYOZEXSTI3/graph.json","events_json":"https://pith.science/api/pith-number/KRGKATKN3PGCHCFBCYOZEXSTI3/events.json","paper":"https://pith.science/paper/KRGKATKN"},"agent_actions":{"view_html":"https://pith.science/pith/KRGKATKN3PGCHCFBCYOZEXSTI3","download_json":"https://pith.science/pith/KRGKATKN3PGCHCFBCYOZEXSTI3.json","view_paper":"https://pith.science/paper/KRGKATKN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.26661&json=true","fetch_graph":"https://pith.science/api/pith-number/KRGKATKN3PGCHCFBCYOZEXSTI3/graph.json","fetch_events":"https://pith.science/api/pith-number/KRGKATKN3PGCHCFBCYOZEXSTI3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KRGKATKN3PGCHCFBCYOZEXSTI3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KRGKATKN3PGCHCFBCYOZEXSTI3/action/storage_attestation","attest_author":"https://pith.science/pith/KRGKATKN3PGCHCFBCYOZEXSTI3/action/author_attestation","sign_citation":"https://pith.science/pith/KRGKATKN3PGCHCFBCYOZEXSTI3/action/citation_signature","submit_replication":"https://pith.science/pith/KRGKATKN3PGCHCFBCYOZEXSTI3/action/replication_record"}},"created_at":"2026-06-26T01:15:56.158782+00:00","updated_at":"2026-06-26T01:15:56.158782+00:00"}