{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:ZWHAEYDHC344RHWXUJRBSGMBNB","short_pith_number":"pith:ZWHAEYDH","schema_version":"1.0","canonical_sha256":"cd8e02606716f9c89ed7a262191981685e7ca93cf33157f87b2245c10956123c","source":{"kind":"arxiv","id":"2111.04611","version":2},"attestation_state":"computed","paper":{"title":"Safety Validation of Autonomous Vehicles using Assertion Checking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Abanoub Ghobrial, Christopher Harper, Greg Chance, Kerstin Eder, Saquib Alam, Tony Pipe","submitted_at":"2021-11-08T16:33:53Z","abstract_excerpt":"Safety and mission performance validation of autonomous vehicles (AVs) is a major challenge. In this paper we describe a methodology for constructing and applying assertion checks to validate the behaviour of an AV operating either in simulation or in the real world. We have identified a taxonomy of assertion types and the general format of their specification, and we have developed procedures for translating driving codes of practice to yield formal logical expressions that can be monitored automatically by computer, either by direct translation or by physical modelling. We have developed exa"},"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":"2111.04611","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2021-11-08T16:33:53Z","cross_cats_sorted":[],"title_canon_sha256":"cc884399223e84049ead95c05953d7c887d3e3ad9cfad582c880e54e8bcc7ad1","abstract_canon_sha256":"48e9822d4c94281538fb1592502b41b63498e1365974a64594049b69a578c509"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:22:52.812540Z","signature_b64":"S6Ya+Cxwl+FXKg/pjJOTCNlAAMqoLWIBk+nyd7c0U9llnaD7lS75TIRGAZOIlcKnE0KCrQf8xeX4t23iGZOGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd8e02606716f9c89ed7a262191981685e7ca93cf33157f87b2245c10956123c","last_reissued_at":"2026-07-05T04:22:52.811966Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:22:52.811966Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Safety Validation of Autonomous Vehicles using Assertion Checking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.DB","authors_text":"Abanoub Ghobrial, Christopher Harper, Greg Chance, Kerstin Eder, Saquib Alam, Tony Pipe","submitted_at":"2021-11-08T16:33:53Z","abstract_excerpt":"Safety and mission performance validation of autonomous vehicles (AVs) is a major challenge. In this paper we describe a methodology for constructing and applying assertion checks to validate the behaviour of an AV operating either in simulation or in the real world. We have identified a taxonomy of assertion types and the general format of their specification, and we have developed procedures for translating driving codes of practice to yield formal logical expressions that can be monitored automatically by computer, either by direct translation or by physical modelling. We have developed exa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2111.04611","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/2111.04611/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":"2111.04611","created_at":"2026-07-05T04:22:52.812101+00:00"},{"alias_kind":"arxiv_version","alias_value":"2111.04611v2","created_at":"2026-07-05T04:22:52.812101+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2111.04611","created_at":"2026-07-05T04:22:52.812101+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZWHAEYDHC344","created_at":"2026-07-05T04:22:52.812101+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZWHAEYDHC344RHWX","created_at":"2026-07-05T04:22:52.812101+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZWHAEYDH","created_at":"2026-07-05T04:22:52.812101+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.06869","citing_title":"Safety Monitoring of Machine Learning Perception Functions: a Survey","ref_index":82,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZWHAEYDHC344RHWXUJRBSGMBNB","json":"https://pith.science/pith/ZWHAEYDHC344RHWXUJRBSGMBNB.json","graph_json":"https://pith.science/api/pith-number/ZWHAEYDHC344RHWXUJRBSGMBNB/graph.json","events_json":"https://pith.science/api/pith-number/ZWHAEYDHC344RHWXUJRBSGMBNB/events.json","paper":"https://pith.science/paper/ZWHAEYDH"},"agent_actions":{"view_html":"https://pith.science/pith/ZWHAEYDHC344RHWXUJRBSGMBNB","download_json":"https://pith.science/pith/ZWHAEYDHC344RHWXUJRBSGMBNB.json","view_paper":"https://pith.science/paper/ZWHAEYDH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2111.04611&json=true","fetch_graph":"https://pith.science/api/pith-number/ZWHAEYDHC344RHWXUJRBSGMBNB/graph.json","fetch_events":"https://pith.science/api/pith-number/ZWHAEYDHC344RHWXUJRBSGMBNB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZWHAEYDHC344RHWXUJRBSGMBNB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZWHAEYDHC344RHWXUJRBSGMBNB/action/storage_attestation","attest_author":"https://pith.science/pith/ZWHAEYDHC344RHWXUJRBSGMBNB/action/author_attestation","sign_citation":"https://pith.science/pith/ZWHAEYDHC344RHWXUJRBSGMBNB/action/citation_signature","submit_replication":"https://pith.science/pith/ZWHAEYDHC344RHWXUJRBSGMBNB/action/replication_record"}},"created_at":"2026-07-05T04:22:52.812101+00:00","updated_at":"2026-07-05T04:22:52.812101+00:00"}