{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AA4SNIBAL55DFFBIDGRLUIQGLO","short_pith_number":"pith:AA4SNIBA","schema_version":"1.0","canonical_sha256":"003926a0205f7a32942819a2ba22065babd80e5d93d7704377b09a2ddd719766","source":{"kind":"arxiv","id":"2411.10031","version":1},"attestation_state":"computed","paper":{"title":"Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Jingyuan Zhou, Jinhao Liang, Kaidi Yang, Longhao Yan","submitted_at":"2024-11-15T08:19:07Z","abstract_excerpt":"It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be a promising control strategy because it can manage complex scenarios in real time. However, current research on MARL-based mixed-autonomy platoon control suffers from several limitations. First, existing MARL approaches address safety by penalizing safety violations in the reward function, thus lacking theoretical safety guarantees due to the blac"},"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":"2411.10031","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2024-11-15T08:19:07Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"b7e734212e286f8ee48f1d6248a12df70d65e3b9e82e204905fb03ddaccb9d0d","abstract_canon_sha256":"84d97c281a62ef5e488fa582db774f5e410af7518cd1f23391c907193774485d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:35:57.422339Z","signature_b64":"mbgmIiSEEPvaCxl0mRZF2YCWCPJJ2nZTRKwf4FJFNyiRTdLf6idR64Gs8BrMi48ft9KfTYx+NTujd7SniN+ODQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"003926a0205f7a32942819a2ba22065babd80e5d93d7704377b09a2ddd719766","last_reissued_at":"2026-07-05T09:35:57.421867Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:35:57.421867Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Jingyuan Zhou, Jinhao Liang, Kaidi Yang, Longhao Yan","submitted_at":"2024-11-15T08:19:07Z","abstract_excerpt":"It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be a promising control strategy because it can manage complex scenarios in real time. However, current research on MARL-based mixed-autonomy platoon control suffers from several limitations. First, existing MARL approaches address safety by penalizing safety violations in the reward function, thus lacking theoretical safety guarantees due to the blac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.10031","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/2411.10031/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":"2411.10031","created_at":"2026-07-05T09:35:57.421921+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.10031v1","created_at":"2026-07-05T09:35:57.421921+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.10031","created_at":"2026-07-05T09:35:57.421921+00:00"},{"alias_kind":"pith_short_12","alias_value":"AA4SNIBAL55D","created_at":"2026-07-05T09:35:57.421921+00:00"},{"alias_kind":"pith_short_16","alias_value":"AA4SNIBAL55DFFBI","created_at":"2026-07-05T09:35:57.421921+00:00"},{"alias_kind":"pith_short_8","alias_value":"AA4SNIBA","created_at":"2026-07-05T09:35:57.421921+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.16311","citing_title":"Towards Emergency Scenarios: An Integrated Decision-making Framework of Multi-lane Platoon Reorganization","ref_index":38,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AA4SNIBAL55DFFBIDGRLUIQGLO","json":"https://pith.science/pith/AA4SNIBAL55DFFBIDGRLUIQGLO.json","graph_json":"https://pith.science/api/pith-number/AA4SNIBAL55DFFBIDGRLUIQGLO/graph.json","events_json":"https://pith.science/api/pith-number/AA4SNIBAL55DFFBIDGRLUIQGLO/events.json","paper":"https://pith.science/paper/AA4SNIBA"},"agent_actions":{"view_html":"https://pith.science/pith/AA4SNIBAL55DFFBIDGRLUIQGLO","download_json":"https://pith.science/pith/AA4SNIBAL55DFFBIDGRLUIQGLO.json","view_paper":"https://pith.science/paper/AA4SNIBA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.10031&json=true","fetch_graph":"https://pith.science/api/pith-number/AA4SNIBAL55DFFBIDGRLUIQGLO/graph.json","fetch_events":"https://pith.science/api/pith-number/AA4SNIBAL55DFFBIDGRLUIQGLO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AA4SNIBAL55DFFBIDGRLUIQGLO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AA4SNIBAL55DFFBIDGRLUIQGLO/action/storage_attestation","attest_author":"https://pith.science/pith/AA4SNIBAL55DFFBIDGRLUIQGLO/action/author_attestation","sign_citation":"https://pith.science/pith/AA4SNIBAL55DFFBIDGRLUIQGLO/action/citation_signature","submit_replication":"https://pith.science/pith/AA4SNIBAL55DFFBIDGRLUIQGLO/action/replication_record"}},"created_at":"2026-07-05T09:35:57.421921+00:00","updated_at":"2026-07-05T09:35:57.421921+00:00"}