{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UWCNVK26TMOU3WPQSDLNKJGXNX","short_pith_number":"pith:UWCNVK26","schema_version":"1.0","canonical_sha256":"a584daab5e9b1d4dd9f090d6d524d76ddfbe4f4842fbd24c071e409772bbd3f2","source":{"kind":"arxiv","id":"2409.17630","version":2},"attestation_state":"computed","paper":{"title":"System-Level Safety Monitoring and Recovery for Perception Failures in Autonomous Vehicles","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Apoorva Sharma, Boris Ivanovic, Kaustav Chakraborty, Marco Pavone, Somil Bansal, Sushant Veer, Zeyuan Feng","submitted_at":"2024-09-26T08:25:05Z","abstract_excerpt":"The safety-critical nature of autonomous vehicle (AV) operation necessitates development of task-relevant algorithms that can reason about safety at the system level and not just at the component level. To reason about the impact of a perception failure on the entire system performance, such task-relevant algorithms must contend with various challenges: complexity of AV stacks, high uncertainty in the operating environments, and the need for real-time performance. To overcome these challenges, in this work, we introduce a Q-network called SPARQ (abbreviation for Safety evaluation for Perceptio"},"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":"2409.17630","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2024-09-26T08:25:05Z","cross_cats_sorted":[],"title_canon_sha256":"1b8c655a79470831bf2307a60db22dc939b5b3db811ea9d3697308c0f1e4d799","abstract_canon_sha256":"74257f91c8052e749b5d1549be410216ad0ddfad4e09172c41638270de574d2e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:16:21.817879Z","signature_b64":"WrprvCpZq6drZA0SPCRA8bNpNR/Lqt0IXiEFndispAKcgZnNYya0boR7tKD7DYK13HqFP4bqhx5AJHdrpmzJAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a584daab5e9b1d4dd9f090d6d524d76ddfbe4f4842fbd24c071e409772bbd3f2","last_reissued_at":"2026-07-05T09:16:21.817320Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:16:21.817320Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"System-Level Safety Monitoring and Recovery for Perception Failures in Autonomous Vehicles","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Apoorva Sharma, Boris Ivanovic, Kaustav Chakraborty, Marco Pavone, Somil Bansal, Sushant Veer, Zeyuan Feng","submitted_at":"2024-09-26T08:25:05Z","abstract_excerpt":"The safety-critical nature of autonomous vehicle (AV) operation necessitates development of task-relevant algorithms that can reason about safety at the system level and not just at the component level. To reason about the impact of a perception failure on the entire system performance, such task-relevant algorithms must contend with various challenges: complexity of AV stacks, high uncertainty in the operating environments, and the need for real-time performance. To overcome these challenges, in this work, we introduce a Q-network called SPARQ (abbreviation for Safety evaluation for Perceptio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.17630","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/2409.17630/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":"2409.17630","created_at":"2026-07-05T09:16:21.817380+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.17630v2","created_at":"2026-07-05T09:16:21.817380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.17630","created_at":"2026-07-05T09:16:21.817380+00:00"},{"alias_kind":"pith_short_12","alias_value":"UWCNVK26TMOU","created_at":"2026-07-05T09:16:21.817380+00:00"},{"alias_kind":"pith_short_16","alias_value":"UWCNVK26TMOU3WPQ","created_at":"2026-07-05T09:16:21.817380+00:00"},{"alias_kind":"pith_short_8","alias_value":"UWCNVK26","created_at":"2026-07-05T09:16:21.817380+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.22389","citing_title":"Safety Evaluation of Motion Plans Using Trajectory Predictors as Forward Reachable Set Estimators","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UWCNVK26TMOU3WPQSDLNKJGXNX","json":"https://pith.science/pith/UWCNVK26TMOU3WPQSDLNKJGXNX.json","graph_json":"https://pith.science/api/pith-number/UWCNVK26TMOU3WPQSDLNKJGXNX/graph.json","events_json":"https://pith.science/api/pith-number/UWCNVK26TMOU3WPQSDLNKJGXNX/events.json","paper":"https://pith.science/paper/UWCNVK26"},"agent_actions":{"view_html":"https://pith.science/pith/UWCNVK26TMOU3WPQSDLNKJGXNX","download_json":"https://pith.science/pith/UWCNVK26TMOU3WPQSDLNKJGXNX.json","view_paper":"https://pith.science/paper/UWCNVK26","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.17630&json=true","fetch_graph":"https://pith.science/api/pith-number/UWCNVK26TMOU3WPQSDLNKJGXNX/graph.json","fetch_events":"https://pith.science/api/pith-number/UWCNVK26TMOU3WPQSDLNKJGXNX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UWCNVK26TMOU3WPQSDLNKJGXNX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UWCNVK26TMOU3WPQSDLNKJGXNX/action/storage_attestation","attest_author":"https://pith.science/pith/UWCNVK26TMOU3WPQSDLNKJGXNX/action/author_attestation","sign_citation":"https://pith.science/pith/UWCNVK26TMOU3WPQSDLNKJGXNX/action/citation_signature","submit_replication":"https://pith.science/pith/UWCNVK26TMOU3WPQSDLNKJGXNX/action/replication_record"}},"created_at":"2026-07-05T09:16:21.817380+00:00","updated_at":"2026-07-05T09:16:21.817380+00:00"}