{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OJFKMMWZOEB6DI7EAMEUAH2V6X","short_pith_number":"pith:OJFKMMWZ","schema_version":"1.0","canonical_sha256":"724aa632d97103e1a3e40309401f55f5f3c342fb7853c5b174fa9d3d545f4164","source":{"kind":"arxiv","id":"2606.27577","version":1},"attestation_state":"computed","paper":{"title":"hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freeways","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hoang H. Nguyen, Mahshid Malazizi, Mina Sartipi, Seyedmehdi Khaleghian, Toru Hirano, Yunfei Xu","submitted_at":"2026-06-25T22:04:54Z","abstract_excerpt":"This paper formulates frame-level freeway risk assessment as a multi-agent scene graph-level binary classification problem, where each video or trajectory frame is labeled risky if any TTC- or PET-based conflict violates a specified severity threshold. We construct a relation-aware graph per frame with vehicles as nodes and two interaction types as edges: same-lane (longitudinal) and adjacent-lane (lateral), augmented with physics-informed edge features aligned to rear-end and lane-change conflict mechanisms. Building on a structured benchmarking suite of non-graph models and graph baselines, "},"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":"2606.27577","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-25T22:04:54Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"129bf5e87caa2433c27c23352c808c2e8e6044a9c7e76952324eee0276cfb24f","abstract_canon_sha256":"9c696f0699e19c09b62c6508b83dc108d1c7cde33171d29f54ea3c4a3866c78c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-29T00:14:10.523638Z","signature_b64":"gX59sw4Pzs6kF4SOWtZXOPDGaASMnQhWT3+jANTyow/wngmITGVWD4IUp1gvaF1ndQImEZT/rGfeptMZHWNxBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"724aa632d97103e1a3e40309401f55f5f3c342fb7853c5b174fa9d3d545f4164","last_reissued_at":"2026-06-29T00:14:10.523262Z","signature_status":"signed_v1","first_computed_at":"2026-06-29T00:14:10.523262Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"hia-gat: A Heterogeneous Interaction-Aware Graph Attention Network For Frame-Level Traffic Conflict Risk Prediction On Freeways","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hoang H. Nguyen, Mahshid Malazizi, Mina Sartipi, Seyedmehdi Khaleghian, Toru Hirano, Yunfei Xu","submitted_at":"2026-06-25T22:04:54Z","abstract_excerpt":"This paper formulates frame-level freeway risk assessment as a multi-agent scene graph-level binary classification problem, where each video or trajectory frame is labeled risky if any TTC- or PET-based conflict violates a specified severity threshold. We construct a relation-aware graph per frame with vehicles as nodes and two interaction types as edges: same-lane (longitudinal) and adjacent-lane (lateral), augmented with physics-informed edge features aligned to rear-end and lane-change conflict mechanisms. Building on a structured benchmarking suite of non-graph models and graph baselines, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.27577","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/2606.27577/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":"2606.27577","created_at":"2026-06-29T00:14:10.523324+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.27577v1","created_at":"2026-06-29T00:14:10.523324+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.27577","created_at":"2026-06-29T00:14:10.523324+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJFKMMWZOEB6","created_at":"2026-06-29T00:14:10.523324+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJFKMMWZOEB6DI7E","created_at":"2026-06-29T00:14:10.523324+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJFKMMWZ","created_at":"2026-06-29T00:14:10.523324+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/OJFKMMWZOEB6DI7EAMEUAH2V6X","json":"https://pith.science/pith/OJFKMMWZOEB6DI7EAMEUAH2V6X.json","graph_json":"https://pith.science/api/pith-number/OJFKMMWZOEB6DI7EAMEUAH2V6X/graph.json","events_json":"https://pith.science/api/pith-number/OJFKMMWZOEB6DI7EAMEUAH2V6X/events.json","paper":"https://pith.science/paper/OJFKMMWZ"},"agent_actions":{"view_html":"https://pith.science/pith/OJFKMMWZOEB6DI7EAMEUAH2V6X","download_json":"https://pith.science/pith/OJFKMMWZOEB6DI7EAMEUAH2V6X.json","view_paper":"https://pith.science/paper/OJFKMMWZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.27577&json=true","fetch_graph":"https://pith.science/api/pith-number/OJFKMMWZOEB6DI7EAMEUAH2V6X/graph.json","fetch_events":"https://pith.science/api/pith-number/OJFKMMWZOEB6DI7EAMEUAH2V6X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJFKMMWZOEB6DI7EAMEUAH2V6X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJFKMMWZOEB6DI7EAMEUAH2V6X/action/storage_attestation","attest_author":"https://pith.science/pith/OJFKMMWZOEB6DI7EAMEUAH2V6X/action/author_attestation","sign_citation":"https://pith.science/pith/OJFKMMWZOEB6DI7EAMEUAH2V6X/action/citation_signature","submit_replication":"https://pith.science/pith/OJFKMMWZOEB6DI7EAMEUAH2V6X/action/replication_record"}},"created_at":"2026-06-29T00:14:10.523324+00:00","updated_at":"2026-06-29T00:14:10.523324+00:00"}