{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:O7UBBVKI3PVOBDARQ72N2ICVES","short_pith_number":"pith:O7UBBVKI","schema_version":"1.0","canonical_sha256":"77e810d548dbeae08c1187f4dd205524a51e1be1d7f829a513edd6e642e48618","source":{"kind":"arxiv","id":"2310.05170","version":1},"attestation_state":"computed","paper":{"title":"DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.SE","authors_text":"Chengjie Lu, Man Zhang, Shaukat Ali, Tao Yue","submitted_at":"2023-10-08T13:59:43Z","abstract_excerpt":"Autonomous driving systems (ADSs) are capable of sensing the environment and making driving decisions autonomously. These systems are safety-critical, and testing them is one of the important approaches to ensure their safety. However, due to the inherent complexity of ADSs and the high dimensionality of their operating environment, the number of possible test scenarios for ADSs is infinite. Besides, the operating environment of ADSs is dynamic, continuously evolving, and full of uncertainties, which requires a testing approach adaptive to the environment. In addition, existing ADS testing tec"},"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":"2310.05170","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2023-10-08T13:59:43Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"90522be407141474b82e4c4f0deaf8b6d7d24dcd4e6da8d99459db13cdaed3bf","abstract_canon_sha256":"d55ae6650c1ec9bde834592ffb73553df44258e440c16341dd8f3b2b2ac7b33c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:58:27.698127Z","signature_b64":"kix3I542a/5yEuXp4aUCu19WwGkWa0jOe00DhFxOgnivWRXZouq7umxu1tC9TrpfseSPTCmQxN+9Jy2lwQMUCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77e810d548dbeae08c1187f4dd205524a51e1be1d7f829a513edd6e642e48618","last_reissued_at":"2026-07-05T06:58:27.697672Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:58:27.697672Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.SE","authors_text":"Chengjie Lu, Man Zhang, Shaukat Ali, Tao Yue","submitted_at":"2023-10-08T13:59:43Z","abstract_excerpt":"Autonomous driving systems (ADSs) are capable of sensing the environment and making driving decisions autonomously. These systems are safety-critical, and testing them is one of the important approaches to ensure their safety. However, due to the inherent complexity of ADSs and the high dimensionality of their operating environment, the number of possible test scenarios for ADSs is infinite. Besides, the operating environment of ADSs is dynamic, continuously evolving, and full of uncertainties, which requires a testing approach adaptive to the environment. In addition, existing ADS testing tec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.05170","kind":"arxiv","version":1},"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/2310.05170/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":"2310.05170","created_at":"2026-07-05T06:58:27.697723+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.05170v1","created_at":"2026-07-05T06:58:27.697723+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.05170","created_at":"2026-07-05T06:58:27.697723+00:00"},{"alias_kind":"pith_short_12","alias_value":"O7UBBVKI3PVO","created_at":"2026-07-05T06:58:27.697723+00:00"},{"alias_kind":"pith_short_16","alias_value":"O7UBBVKI3PVOBDAR","created_at":"2026-07-05T06:58:27.697723+00:00"},{"alias_kind":"pith_short_8","alias_value":"O7UBBVKI","created_at":"2026-07-05T06:58:27.697723+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O7UBBVKI3PVOBDARQ72N2ICVES","json":"https://pith.science/pith/O7UBBVKI3PVOBDARQ72N2ICVES.json","graph_json":"https://pith.science/api/pith-number/O7UBBVKI3PVOBDARQ72N2ICVES/graph.json","events_json":"https://pith.science/api/pith-number/O7UBBVKI3PVOBDARQ72N2ICVES/events.json","paper":"https://pith.science/paper/O7UBBVKI"},"agent_actions":{"view_html":"https://pith.science/pith/O7UBBVKI3PVOBDARQ72N2ICVES","download_json":"https://pith.science/pith/O7UBBVKI3PVOBDARQ72N2ICVES.json","view_paper":"https://pith.science/paper/O7UBBVKI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.05170&json=true","fetch_graph":"https://pith.science/api/pith-number/O7UBBVKI3PVOBDARQ72N2ICVES/graph.json","fetch_events":"https://pith.science/api/pith-number/O7UBBVKI3PVOBDARQ72N2ICVES/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O7UBBVKI3PVOBDARQ72N2ICVES/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O7UBBVKI3PVOBDARQ72N2ICVES/action/storage_attestation","attest_author":"https://pith.science/pith/O7UBBVKI3PVOBDARQ72N2ICVES/action/author_attestation","sign_citation":"https://pith.science/pith/O7UBBVKI3PVOBDARQ72N2ICVES/action/citation_signature","submit_replication":"https://pith.science/pith/O7UBBVKI3PVOBDARQ72N2ICVES/action/replication_record"}},"created_at":"2026-07-05T06:58:27.697723+00:00","updated_at":"2026-07-05T06:58:27.697723+00:00"}