{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SDI5CTNW2A7EMRPUJGF6LNM3QV","short_pith_number":"pith:SDI5CTNW","schema_version":"1.0","canonical_sha256":"90d1d14db6d03e4645f4498be5b59b85532ba0dd219f7d4beb84d6e5463f75a9","source":{"kind":"arxiv","id":"2312.13156","version":3},"attestation_state":"computed","paper":{"title":"AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CE","authors_text":"Daocheng Fu, Haiyang Yu, Hanchu Zhou, Han Jiang, Helai Huang, Lening Wang, Pinlong Cai, Tianqi Wang, Xuesong Wang, Yilong Ren, Yinhai Wang, Zhiyong Cui","submitted_at":"2023-12-20T16:19:47Z","abstract_excerpt":"Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic safety. However, previous studies, whether focusing on static environmental assessments or dynamic driving analyses, as well as pre-accident predictions or post-accident rule analyses, have typically been conducted in isolation. There has been a lack of an effective framework for developing a comprehensive understanding and application of traffic safety. To address this gap, this paper introduces AccidentGPT, a compr"},"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":"2312.13156","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2023-12-20T16:19:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a8a07c54db83ba2320bced0cf64d29c6a70d05df07b66e343a4bf9126ddafe12","abstract_canon_sha256":"3d7576ee45e696f7ba1cd9b80a4cb11fa0114d5881870e010b0b31421e5b2bf6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:28:47.629872Z","signature_b64":"TPVySbvS+Bn0Xt7VUD6sgWBe0Jbi9xC6USEVwog7dSWQUVWXTJj6Vugax/6raJKI5bGhcQzonfNWnwK+qqwlBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90d1d14db6d03e4645f4498be5b59b85532ba0dd219f7d4beb84d6e5463f75a9","last_reissued_at":"2026-07-05T07:28:47.629298Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:28:47.629298Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AccidentGPT: Accident Analysis and Prevention from V2X Environmental Perception with Multi-modal Large Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CE","authors_text":"Daocheng Fu, Haiyang Yu, Hanchu Zhou, Han Jiang, Helai Huang, Lening Wang, Pinlong Cai, Tianqi Wang, Xuesong Wang, Yilong Ren, Yinhai Wang, Zhiyong Cui","submitted_at":"2023-12-20T16:19:47Z","abstract_excerpt":"Traffic accidents, being a significant contributor to both human casualties and property damage, have long been a focal point of research for many scholars in the field of traffic safety. However, previous studies, whether focusing on static environmental assessments or dynamic driving analyses, as well as pre-accident predictions or post-accident rule analyses, have typically been conducted in isolation. There has been a lack of an effective framework for developing a comprehensive understanding and application of traffic safety. To address this gap, this paper introduces AccidentGPT, a compr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.13156","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/2312.13156/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":"2312.13156","created_at":"2026-07-05T07:28:47.629373+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.13156v3","created_at":"2026-07-05T07:28:47.629373+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.13156","created_at":"2026-07-05T07:28:47.629373+00:00"},{"alias_kind":"pith_short_12","alias_value":"SDI5CTNW2A7E","created_at":"2026-07-05T07:28:47.629373+00:00"},{"alias_kind":"pith_short_16","alias_value":"SDI5CTNW2A7EMRPU","created_at":"2026-07-05T07:28:47.629373+00:00"},{"alias_kind":"pith_short_8","alias_value":"SDI5CTNW","created_at":"2026-07-05T07:28:47.629373+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00991","citing_title":"Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support","ref_index":56,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SDI5CTNW2A7EMRPUJGF6LNM3QV","json":"https://pith.science/pith/SDI5CTNW2A7EMRPUJGF6LNM3QV.json","graph_json":"https://pith.science/api/pith-number/SDI5CTNW2A7EMRPUJGF6LNM3QV/graph.json","events_json":"https://pith.science/api/pith-number/SDI5CTNW2A7EMRPUJGF6LNM3QV/events.json","paper":"https://pith.science/paper/SDI5CTNW"},"agent_actions":{"view_html":"https://pith.science/pith/SDI5CTNW2A7EMRPUJGF6LNM3QV","download_json":"https://pith.science/pith/SDI5CTNW2A7EMRPUJGF6LNM3QV.json","view_paper":"https://pith.science/paper/SDI5CTNW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.13156&json=true","fetch_graph":"https://pith.science/api/pith-number/SDI5CTNW2A7EMRPUJGF6LNM3QV/graph.json","fetch_events":"https://pith.science/api/pith-number/SDI5CTNW2A7EMRPUJGF6LNM3QV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SDI5CTNW2A7EMRPUJGF6LNM3QV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SDI5CTNW2A7EMRPUJGF6LNM3QV/action/storage_attestation","attest_author":"https://pith.science/pith/SDI5CTNW2A7EMRPUJGF6LNM3QV/action/author_attestation","sign_citation":"https://pith.science/pith/SDI5CTNW2A7EMRPUJGF6LNM3QV/action/citation_signature","submit_replication":"https://pith.science/pith/SDI5CTNW2A7EMRPUJGF6LNM3QV/action/replication_record"}},"created_at":"2026-07-05T07:28:47.629373+00:00","updated_at":"2026-07-05T07:28:47.629373+00:00"}