{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:IOJN26U3QRGFTXK7AMSZPXJLJD","short_pith_number":"pith:IOJN26U3","schema_version":"1.0","canonical_sha256":"4392dd7a9b844c59dd5f032597dd2b48ef57707a2af8d47becda8a687ba1ff66","source":{"kind":"arxiv","id":"2303.13694","version":2},"attestation_state":"computed","paper":{"title":"Ensemble Gaussian Processes for Adaptive Autonomous Driving on Multi-friction Surfaces","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Ahmad Amine, Rahul Mangharam, Tom\\'a\\v{s} Nagy, Truong X. Nghiem, Ugo Rosolia, Zirui Zang","submitted_at":"2023-03-23T22:13:12Z","abstract_excerpt":"Driving under varying road conditions is challenging, especially for autonomous vehicles that must adapt in real-time to changes in the environment, e.g., rain, snow, etc. It is difficult to apply offline learning-based methods in these time-varying settings, as the controller should be trained on datasets representing all conditions it might encounter in the future. While online learning may adapt a model from real-time data, its convergence is often too slow for fast varying road conditions. We study this problem in autonomous racing, where driving at the limits of handling under varying roa"},"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":"2303.13694","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2023-03-23T22:13:12Z","cross_cats_sorted":["cs.SY","eess.SY"],"title_canon_sha256":"6b0142865bb9b0150fa8393b522a5c871759cfcf0c80c80f7c22ff1fc52189fc","abstract_canon_sha256":"f273438b970e0abb60b63e16a753d562e1ebd64d0f965c50c26a915df0a567b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:24.138752Z","signature_b64":"9WSvh3I3TGtRozeGFIAUJiU6dGJCEAEMJarL93eybeUzyJIGTCHh/UkC2rAXwGqHB66QvCQAHI6G8/8l4wgeCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4392dd7a9b844c59dd5f032597dd2b48ef57707a2af8d47becda8a687ba1ff66","last_reissued_at":"2026-07-05T06:14:24.138344Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:24.138344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Ensemble Gaussian Processes for Adaptive Autonomous Driving on Multi-friction Surfaces","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Ahmad Amine, Rahul Mangharam, Tom\\'a\\v{s} Nagy, Truong X. Nghiem, Ugo Rosolia, Zirui Zang","submitted_at":"2023-03-23T22:13:12Z","abstract_excerpt":"Driving under varying road conditions is challenging, especially for autonomous vehicles that must adapt in real-time to changes in the environment, e.g., rain, snow, etc. It is difficult to apply offline learning-based methods in these time-varying settings, as the controller should be trained on datasets representing all conditions it might encounter in the future. While online learning may adapt a model from real-time data, its convergence is often too slow for fast varying road conditions. We study this problem in autonomous racing, where driving at the limits of handling under varying roa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.13694","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/2303.13694/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":"2303.13694","created_at":"2026-07-05T06:14:24.138401+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.13694v2","created_at":"2026-07-05T06:14:24.138401+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.13694","created_at":"2026-07-05T06:14:24.138401+00:00"},{"alias_kind":"pith_short_12","alias_value":"IOJN26U3QRGF","created_at":"2026-07-05T06:14:24.138401+00:00"},{"alias_kind":"pith_short_16","alias_value":"IOJN26U3QRGFTXK7","created_at":"2026-07-05T06:14:24.138401+00:00"},{"alias_kind":"pith_short_8","alias_value":"IOJN26U3","created_at":"2026-07-05T06:14:24.138401+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/IOJN26U3QRGFTXK7AMSZPXJLJD","json":"https://pith.science/pith/IOJN26U3QRGFTXK7AMSZPXJLJD.json","graph_json":"https://pith.science/api/pith-number/IOJN26U3QRGFTXK7AMSZPXJLJD/graph.json","events_json":"https://pith.science/api/pith-number/IOJN26U3QRGFTXK7AMSZPXJLJD/events.json","paper":"https://pith.science/paper/IOJN26U3"},"agent_actions":{"view_html":"https://pith.science/pith/IOJN26U3QRGFTXK7AMSZPXJLJD","download_json":"https://pith.science/pith/IOJN26U3QRGFTXK7AMSZPXJLJD.json","view_paper":"https://pith.science/paper/IOJN26U3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.13694&json=true","fetch_graph":"https://pith.science/api/pith-number/IOJN26U3QRGFTXK7AMSZPXJLJD/graph.json","fetch_events":"https://pith.science/api/pith-number/IOJN26U3QRGFTXK7AMSZPXJLJD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IOJN26U3QRGFTXK7AMSZPXJLJD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IOJN26U3QRGFTXK7AMSZPXJLJD/action/storage_attestation","attest_author":"https://pith.science/pith/IOJN26U3QRGFTXK7AMSZPXJLJD/action/author_attestation","sign_citation":"https://pith.science/pith/IOJN26U3QRGFTXK7AMSZPXJLJD/action/citation_signature","submit_replication":"https://pith.science/pith/IOJN26U3QRGFTXK7AMSZPXJLJD/action/replication_record"}},"created_at":"2026-07-05T06:14:24.138401+00:00","updated_at":"2026-07-05T06:14:24.138401+00:00"}