{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VKIAV5KQZMJ5H44HNVFHUPKWWK","short_pith_number":"pith:VKIAV5KQ","schema_version":"1.0","canonical_sha256":"aa900af550cb13d3f3876d4a7a3d56b2a740be12135d70283c93c10e2560be28","source":{"kind":"arxiv","id":"2404.07762","version":4},"attestation_state":"computed","paper":{"title":"NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam Tonderski, Christoffer Petersson, Holger Caesar, Joakim Johnander, Kalle {\\AA}str\\\"om, Michael Felsberg, William Ljungbergh","submitted_at":"2024-04-11T14:03:16Z","abstract_excerpt":"We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios. The simulator learns from sequences of real-world driving sensor data and enables reconfigurations and renderings of new, unseen scenarios. In this work, we use our simulator to test the responses of AD models to safety-critical scenarios inspired by the European New Car Assessment Programme (Euro NCAP). Our evaluation reveals that, while state-of-the-art end-to-end planners excel in nom"},"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":"2404.07762","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-04-11T14:03:16Z","cross_cats_sorted":[],"title_canon_sha256":"8cf149c0f773639e2a8edd46769222d652fb10e93e8a53ec00cb080e501f8f10","abstract_canon_sha256":"abc6fa4f82e2532fc15bb235561e77871426bc1dcff5b8d6b0fcb67ca5e8a9d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:03.445673Z","signature_b64":"/IoOUr7CBDVZYZW+JgKRNriGgTaFMYTLuINw8lNG6pIyOXB4GWwIAQkg2Oyg4lMA0n0tho231GAX1o4DpGssDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa900af550cb13d3f3876d4a7a3d56b2a740be12135d70283c93c10e2560be28","last_reissued_at":"2026-07-05T08:11:03.445168Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:03.445168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NeuroNCAP: Photorealistic Closed-loop Safety Testing for Autonomous Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adam Tonderski, Christoffer Petersson, Holger Caesar, Joakim Johnander, Kalle {\\AA}str\\\"om, Michael Felsberg, William Ljungbergh","submitted_at":"2024-04-11T14:03:16Z","abstract_excerpt":"We present a versatile NeRF-based simulator for testing autonomous driving (AD) software systems, designed with a focus on sensor-realistic closed-loop evaluation and the creation of safety-critical scenarios. The simulator learns from sequences of real-world driving sensor data and enables reconfigurations and renderings of new, unseen scenarios. In this work, we use our simulator to test the responses of AD models to safety-critical scenarios inspired by the European New Car Assessment Programme (Euro NCAP). Our evaluation reveals that, while state-of-the-art end-to-end planners excel in nom"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.07762","kind":"arxiv","version":4},"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/2404.07762/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":"2404.07762","created_at":"2026-07-05T08:11:03.445224+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.07762v4","created_at":"2026-07-05T08:11:03.445224+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.07762","created_at":"2026-07-05T08:11:03.445224+00:00"},{"alias_kind":"pith_short_12","alias_value":"VKIAV5KQZMJ5","created_at":"2026-07-05T08:11:03.445224+00:00"},{"alias_kind":"pith_short_16","alias_value":"VKIAV5KQZMJ5H44H","created_at":"2026-07-05T08:11:03.445224+00:00"},{"alias_kind":"pith_short_8","alias_value":"VKIAV5KQ","created_at":"2026-07-05T08:11:03.445224+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.19292","citing_title":"UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VKIAV5KQZMJ5H44HNVFHUPKWWK","json":"https://pith.science/pith/VKIAV5KQZMJ5H44HNVFHUPKWWK.json","graph_json":"https://pith.science/api/pith-number/VKIAV5KQZMJ5H44HNVFHUPKWWK/graph.json","events_json":"https://pith.science/api/pith-number/VKIAV5KQZMJ5H44HNVFHUPKWWK/events.json","paper":"https://pith.science/paper/VKIAV5KQ"},"agent_actions":{"view_html":"https://pith.science/pith/VKIAV5KQZMJ5H44HNVFHUPKWWK","download_json":"https://pith.science/pith/VKIAV5KQZMJ5H44HNVFHUPKWWK.json","view_paper":"https://pith.science/paper/VKIAV5KQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.07762&json=true","fetch_graph":"https://pith.science/api/pith-number/VKIAV5KQZMJ5H44HNVFHUPKWWK/graph.json","fetch_events":"https://pith.science/api/pith-number/VKIAV5KQZMJ5H44HNVFHUPKWWK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VKIAV5KQZMJ5H44HNVFHUPKWWK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VKIAV5KQZMJ5H44HNVFHUPKWWK/action/storage_attestation","attest_author":"https://pith.science/pith/VKIAV5KQZMJ5H44HNVFHUPKWWK/action/author_attestation","sign_citation":"https://pith.science/pith/VKIAV5KQZMJ5H44HNVFHUPKWWK/action/citation_signature","submit_replication":"https://pith.science/pith/VKIAV5KQZMJ5H44HNVFHUPKWWK/action/replication_record"}},"created_at":"2026-07-05T08:11:03.445224+00:00","updated_at":"2026-07-05T08:11:03.445224+00:00"}