{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SDP4IOC2TW5DMJQYBAXMYXQWGI","short_pith_number":"pith:SDP4IOC2","schema_version":"1.0","canonical_sha256":"90dfc4385a9dba362618082ecc5e16323d88f2bacbe84d77fffbe17bd415d30a","source":{"kind":"arxiv","id":"2406.10857","version":2},"attestation_state":"computed","paper":{"title":"LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"An Guo, Guoquan Wu, Haoxiang Tian, Jun Wei, Mingfei Cheng, Shuo Li, Tianwei Zhang, Xingshuo Han, Yuan Zhou","submitted_at":"2024-06-16T09:05:56Z","abstract_excerpt":"Safety testing serves as the fundamental pillar for the development of autonomous driving systems (ADSs). To ensure the safety of ADSs, it is paramount to generate a diverse range of safety-critical test scenarios. While existing ADS practitioners primarily focus on reproducing real-world traffic accidents in simulation environments to create test scenarios, it's essential to highlight that many of these accidents do not directly result in safety violations for ADSs due to the differences between human driving and autonomous driving. More importantly, we observe that some accident-free real-wo"},"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":"2406.10857","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-06-16T09:05:56Z","cross_cats_sorted":[],"title_canon_sha256":"85ed33b986eef972664121a34eaaa2094738c6c4682aa3281878254805328d78","abstract_canon_sha256":"95e0f9ce7a5ab4ac023598f16a18ffaad7145fc8a15cea2743859ce1a8a80819"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:57.940201Z","signature_b64":"2sQbpyCUF4KUS03S8sC0HZTD+jt/t9c8v1m822OKXL6thrUc0WYV0cuWUe+0sj94DWrpS5s0HTnxNL6r/dghAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90dfc4385a9dba362618082ecc5e16323d88f2bacbe84d77fffbe17bd415d30a","last_reissued_at":"2026-07-05T09:55:57.939638Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:57.939638Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LMM-enhanced Safety-Critical Scenario Generation for Autonomous Driving System Testing From Non-Accident Traffic Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"An Guo, Guoquan Wu, Haoxiang Tian, Jun Wei, Mingfei Cheng, Shuo Li, Tianwei Zhang, Xingshuo Han, Yuan Zhou","submitted_at":"2024-06-16T09:05:56Z","abstract_excerpt":"Safety testing serves as the fundamental pillar for the development of autonomous driving systems (ADSs). To ensure the safety of ADSs, it is paramount to generate a diverse range of safety-critical test scenarios. While existing ADS practitioners primarily focus on reproducing real-world traffic accidents in simulation environments to create test scenarios, it's essential to highlight that many of these accidents do not directly result in safety violations for ADSs due to the differences between human driving and autonomous driving. More importantly, we observe that some accident-free real-wo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.10857","kind":"arxiv","version":2},"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/2406.10857/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":"2406.10857","created_at":"2026-07-05T09:55:57.939702+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.10857v2","created_at":"2026-07-05T09:55:57.939702+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.10857","created_at":"2026-07-05T09:55:57.939702+00:00"},{"alias_kind":"pith_short_12","alias_value":"SDP4IOC2TW5D","created_at":"2026-07-05T09:55:57.939702+00:00"},{"alias_kind":"pith_short_16","alias_value":"SDP4IOC2TW5DMJQY","created_at":"2026-07-05T09:55:57.939702+00:00"},{"alias_kind":"pith_short_8","alias_value":"SDP4IOC2","created_at":"2026-07-05T09:55:57.939702+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.17209","citing_title":"LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2605.15654","citing_title":"PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SDP4IOC2TW5DMJQYBAXMYXQWGI","json":"https://pith.science/pith/SDP4IOC2TW5DMJQYBAXMYXQWGI.json","graph_json":"https://pith.science/api/pith-number/SDP4IOC2TW5DMJQYBAXMYXQWGI/graph.json","events_json":"https://pith.science/api/pith-number/SDP4IOC2TW5DMJQYBAXMYXQWGI/events.json","paper":"https://pith.science/paper/SDP4IOC2"},"agent_actions":{"view_html":"https://pith.science/pith/SDP4IOC2TW5DMJQYBAXMYXQWGI","download_json":"https://pith.science/pith/SDP4IOC2TW5DMJQYBAXMYXQWGI.json","view_paper":"https://pith.science/paper/SDP4IOC2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.10857&json=true","fetch_graph":"https://pith.science/api/pith-number/SDP4IOC2TW5DMJQYBAXMYXQWGI/graph.json","fetch_events":"https://pith.science/api/pith-number/SDP4IOC2TW5DMJQYBAXMYXQWGI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SDP4IOC2TW5DMJQYBAXMYXQWGI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SDP4IOC2TW5DMJQYBAXMYXQWGI/action/storage_attestation","attest_author":"https://pith.science/pith/SDP4IOC2TW5DMJQYBAXMYXQWGI/action/author_attestation","sign_citation":"https://pith.science/pith/SDP4IOC2TW5DMJQYBAXMYXQWGI/action/citation_signature","submit_replication":"https://pith.science/pith/SDP4IOC2TW5DMJQYBAXMYXQWGI/action/replication_record"}},"created_at":"2026-07-05T09:55:57.939702+00:00","updated_at":"2026-07-05T09:55:57.939702+00:00"}