{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MINR37ITXHRX3U5HKYHTZMLQBC","short_pith_number":"pith:MINR37IT","schema_version":"1.0","canonical_sha256":"621b1dfd13b9e37dd3a7560f3cb17008a9be0394581efa0f8b3fe7b8d303dafa","source":{"kind":"arxiv","id":"2312.01659","version":2},"attestation_state":"computed","paper":{"title":"RiskBench: A Scenario-based Benchmark for Risk Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Chieh-Chi Yang, Chi-Hsi Kung, Hsin-Cheng Lu, Pang-Yuan Pao, Pin-Lun Chen, Shu-Wei Lu, Yi-Ting Chen","submitted_at":"2023-12-04T06:21:22Z","abstract_excerpt":"Intelligent driving systems aim to achieve a zero-collision mobility experience, requiring interdisciplinary efforts to enhance safety performance. This work focuses on risk identification, the process of identifying and analyzing risks stemming from dynamic traffic participants and unexpected events. While significant advances have been made in the community, the current evaluation of different risk identification algorithms uses independent datasets, leading to difficulty in direct comparison and hindering collective progress toward safety performance enhancement. To address this limitation,"},"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":"2312.01659","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-04T06:21:22Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"1a47f2a880c004500eb762796ea04eb43be7f3f49a31bb1f2577b13040c204a1","abstract_canon_sha256":"5f4970afd966201717145233f7d538e8345af26dd979a9921c03513e3dcc7c31"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:52:14.543887Z","signature_b64":"L4oBqqF9qNsatjD4STQVNXy3+s53h3l4wjRPDzO1TDy/M4TUeO13lMNXfwcMuAaf5r6enSXBPLjV4WttxB0VBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"621b1dfd13b9e37dd3a7560f3cb17008a9be0394581efa0f8b3fe7b8d303dafa","last_reissued_at":"2026-07-05T07:52:14.543403Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:52:14.543403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RiskBench: A Scenario-based Benchmark for Risk Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Chieh-Chi Yang, Chi-Hsi Kung, Hsin-Cheng Lu, Pang-Yuan Pao, Pin-Lun Chen, Shu-Wei Lu, Yi-Ting Chen","submitted_at":"2023-12-04T06:21:22Z","abstract_excerpt":"Intelligent driving systems aim to achieve a zero-collision mobility experience, requiring interdisciplinary efforts to enhance safety performance. This work focuses on risk identification, the process of identifying and analyzing risks stemming from dynamic traffic participants and unexpected events. While significant advances have been made in the community, the current evaluation of different risk identification algorithms uses independent datasets, leading to difficulty in direct comparison and hindering collective progress toward safety performance enhancement. To address this limitation,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.01659","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/2312.01659/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":"2312.01659","created_at":"2026-07-05T07:52:14.543464+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.01659v2","created_at":"2026-07-05T07:52:14.543464+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.01659","created_at":"2026-07-05T07:52:14.543464+00:00"},{"alias_kind":"pith_short_12","alias_value":"MINR37ITXHRX","created_at":"2026-07-05T07:52:14.543464+00:00"},{"alias_kind":"pith_short_16","alias_value":"MINR37ITXHRX3U5H","created_at":"2026-07-05T07:52:14.543464+00:00"},{"alias_kind":"pith_short_8","alias_value":"MINR37IT","created_at":"2026-07-05T07:52:14.543464+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.17152","citing_title":"On-Road Object Importance Estimation: A New Dataset and A Model with Multi-Fold Top-Down Guidance","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MINR37ITXHRX3U5HKYHTZMLQBC","json":"https://pith.science/pith/MINR37ITXHRX3U5HKYHTZMLQBC.json","graph_json":"https://pith.science/api/pith-number/MINR37ITXHRX3U5HKYHTZMLQBC/graph.json","events_json":"https://pith.science/api/pith-number/MINR37ITXHRX3U5HKYHTZMLQBC/events.json","paper":"https://pith.science/paper/MINR37IT"},"agent_actions":{"view_html":"https://pith.science/pith/MINR37ITXHRX3U5HKYHTZMLQBC","download_json":"https://pith.science/pith/MINR37ITXHRX3U5HKYHTZMLQBC.json","view_paper":"https://pith.science/paper/MINR37IT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.01659&json=true","fetch_graph":"https://pith.science/api/pith-number/MINR37ITXHRX3U5HKYHTZMLQBC/graph.json","fetch_events":"https://pith.science/api/pith-number/MINR37ITXHRX3U5HKYHTZMLQBC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MINR37ITXHRX3U5HKYHTZMLQBC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MINR37ITXHRX3U5HKYHTZMLQBC/action/storage_attestation","attest_author":"https://pith.science/pith/MINR37ITXHRX3U5HKYHTZMLQBC/action/author_attestation","sign_citation":"https://pith.science/pith/MINR37ITXHRX3U5HKYHTZMLQBC/action/citation_signature","submit_replication":"https://pith.science/pith/MINR37ITXHRX3U5HKYHTZMLQBC/action/replication_record"}},"created_at":"2026-07-05T07:52:14.543464+00:00","updated_at":"2026-07-05T07:52:14.543464+00:00"}