{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SB5IG52XG25JSE3NGXOMAU33WA","short_pith_number":"pith:SB5IG52X","schema_version":"1.0","canonical_sha256":"907a83775736ba99136d35dcc0537bb00e4f772c0309b5852971551108b0d990","source":{"kind":"arxiv","id":"1912.03618","version":2},"attestation_state":"computed","paper":{"title":"Efficient Black-box Assessment of Autonomous Vehicle Safety","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aman Sinha, Justin Norden, Matthew O'Kelly","submitted_at":"2019-12-08T05:12:17Z","abstract_excerpt":"While autonomous vehicle (AV) technology has shown substantial progress, we still lack tools for rigorous and scalable testing. Real-world testing, the $\\textit{de-facto}$ evaluation method, is dangerous to the public. Moreover, due to the rare nature of failures, billions of miles of driving are needed to statistically validate performance claims. Thus, the industry has largely turned to simulation to evaluate AV systems. However, having a simulation stack alone is not a solution. A simulation testing framework needs to prioritize which scenarios to run, learn how the chosen scenarios provide"},"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":"1912.03618","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-08T05:12:17Z","cross_cats_sorted":["cs.RO","stat.ML"],"title_canon_sha256":"5f951efd8c8fb38f84a59f5490e43ae4d3acba42f665fc8d454aa0c7b48f5ecb","abstract_canon_sha256":"ccc3ab87b3f703827b9eaff5fabbe576cc7a833a1683739acc9a3972a123bd36"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:08:25.877533Z","signature_b64":"4dnUOYayNZQBTLjdXd2lcYiWiuABiUkiZ9n63UiJQPdD/FKCXJvuvKPf8OMorQA/2ZCCXjEXH8+9CJe0ltLoCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"907a83775736ba99136d35dcc0537bb00e4f772c0309b5852971551108b0d990","last_reissued_at":"2026-07-05T01:08:25.877116Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:08:25.877116Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Black-box Assessment of Autonomous Vehicle Safety","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aman Sinha, Justin Norden, Matthew O'Kelly","submitted_at":"2019-12-08T05:12:17Z","abstract_excerpt":"While autonomous vehicle (AV) technology has shown substantial progress, we still lack tools for rigorous and scalable testing. Real-world testing, the $\\textit{de-facto}$ evaluation method, is dangerous to the public. Moreover, due to the rare nature of failures, billions of miles of driving are needed to statistically validate performance claims. Thus, the industry has largely turned to simulation to evaluate AV systems. However, having a simulation stack alone is not a solution. A simulation testing framework needs to prioritize which scenarios to run, learn how the chosen scenarios provide"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.03618","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/1912.03618/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":"1912.03618","created_at":"2026-07-05T01:08:25.877177+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.03618v2","created_at":"2026-07-05T01:08:25.877177+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.03618","created_at":"2026-07-05T01:08:25.877177+00:00"},{"alias_kind":"pith_short_12","alias_value":"SB5IG52XG25J","created_at":"2026-07-05T01:08:25.877177+00:00"},{"alias_kind":"pith_short_16","alias_value":"SB5IG52XG25JSE3N","created_at":"2026-07-05T01:08:25.877177+00:00"},{"alias_kind":"pith_short_8","alias_value":"SB5IG52X","created_at":"2026-07-05T01:08:25.877177+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.02154","citing_title":"Failure Probability Estimation for Black-Box Autonomous Systems using State-Dependent Importance Sampling Proposals","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SB5IG52XG25JSE3NGXOMAU33WA","json":"https://pith.science/pith/SB5IG52XG25JSE3NGXOMAU33WA.json","graph_json":"https://pith.science/api/pith-number/SB5IG52XG25JSE3NGXOMAU33WA/graph.json","events_json":"https://pith.science/api/pith-number/SB5IG52XG25JSE3NGXOMAU33WA/events.json","paper":"https://pith.science/paper/SB5IG52X"},"agent_actions":{"view_html":"https://pith.science/pith/SB5IG52XG25JSE3NGXOMAU33WA","download_json":"https://pith.science/pith/SB5IG52XG25JSE3NGXOMAU33WA.json","view_paper":"https://pith.science/paper/SB5IG52X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.03618&json=true","fetch_graph":"https://pith.science/api/pith-number/SB5IG52XG25JSE3NGXOMAU33WA/graph.json","fetch_events":"https://pith.science/api/pith-number/SB5IG52XG25JSE3NGXOMAU33WA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SB5IG52XG25JSE3NGXOMAU33WA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SB5IG52XG25JSE3NGXOMAU33WA/action/storage_attestation","attest_author":"https://pith.science/pith/SB5IG52XG25JSE3NGXOMAU33WA/action/author_attestation","sign_citation":"https://pith.science/pith/SB5IG52XG25JSE3NGXOMAU33WA/action/citation_signature","submit_replication":"https://pith.science/pith/SB5IG52XG25JSE3NGXOMAU33WA/action/replication_record"}},"created_at":"2026-07-05T01:08:25.877177+00:00","updated_at":"2026-07-05T01:08:25.877177+00:00"}