{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DB7SDKZH63MF2IV6W37JTWVXWO","short_pith_number":"pith:DB7SDKZH","schema_version":"1.0","canonical_sha256":"187f21ab27f6d85d22beb6fe99dab7b38309fbfb16cc48e717f9b4a8fa64c4ed","source":{"kind":"arxiv","id":"2402.18558","version":2},"attestation_state":"computed","paper":{"title":"Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Benjamin David Evans, Felix Jahncke, Hendrik Willem Jordaan, Herman Arnold Engelbrecht, Johannes Betz, Marco Caccamo, Raphael Trumpp","submitted_at":"2024-02-28T18:42:46Z","abstract_excerpt":"The F1TENTH autonomous driving platform, consisting of 1:10-scale remote-controlled cars, has evolved into a well-established education and research platform. The many publications and real-world competitions span many domains, from classical path planning to novel learning-based algorithms. Consequently, the field is wide and disjointed, hindering direct comparison of developed methods and making it difficult to assess the state-of-the-art. Therefore, we aim to unify the field by surveying current approaches, describing common methods, and providing benchmark results to facilitate clear compa"},"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":"2402.18558","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2024-02-28T18:42:46Z","cross_cats_sorted":[],"title_canon_sha256":"dd60e19d6c138daff2e7c57be3ee75d29c100c431783f273c5c464dc68870fba","abstract_canon_sha256":"39d6eb9c39eecd55382f5ad583bfcd49e56c9320a0c7c2c1dabd86c4e3d6e3e2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:58.164637Z","signature_b64":"TYvbHek42/L/xD4lbbcLQTbXn8gg0m5dr6hp3bBbQ4GRZVBfNTDsmt4cP83vVSFzfZNWm6lPsH9LTIUN5uciBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"187f21ab27f6d85d22beb6fe99dab7b38309fbfb16cc48e717f9b4a8fa64c4ed","last_reissued_at":"2026-07-05T08:11:58.164165Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:58.164165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Benjamin David Evans, Felix Jahncke, Hendrik Willem Jordaan, Herman Arnold Engelbrecht, Johannes Betz, Marco Caccamo, Raphael Trumpp","submitted_at":"2024-02-28T18:42:46Z","abstract_excerpt":"The F1TENTH autonomous driving platform, consisting of 1:10-scale remote-controlled cars, has evolved into a well-established education and research platform. The many publications and real-world competitions span many domains, from classical path planning to novel learning-based algorithms. Consequently, the field is wide and disjointed, hindering direct comparison of developed methods and making it difficult to assess the state-of-the-art. Therefore, we aim to unify the field by surveying current approaches, describing common methods, and providing benchmark results to facilitate clear compa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.18558","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/2402.18558/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":"2402.18558","created_at":"2026-07-05T08:11:58.164221+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.18558v2","created_at":"2026-07-05T08:11:58.164221+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.18558","created_at":"2026-07-05T08:11:58.164221+00:00"},{"alias_kind":"pith_short_12","alias_value":"DB7SDKZH63MF","created_at":"2026-07-05T08:11:58.164221+00:00"},{"alias_kind":"pith_short_16","alias_value":"DB7SDKZH63MF2IV6","created_at":"2026-07-05T08:11:58.164221+00:00"},{"alias_kind":"pith_short_8","alias_value":"DB7SDKZH","created_at":"2026-07-05T08:11:58.164221+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.24934","citing_title":"TEACar: An Open-Source Autonomous Driving Platform","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.24934","citing_title":"TEACar: An Open-Source Autonomous Driving Platform","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07672","citing_title":"Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DB7SDKZH63MF2IV6W37JTWVXWO","json":"https://pith.science/pith/DB7SDKZH63MF2IV6W37JTWVXWO.json","graph_json":"https://pith.science/api/pith-number/DB7SDKZH63MF2IV6W37JTWVXWO/graph.json","events_json":"https://pith.science/api/pith-number/DB7SDKZH63MF2IV6W37JTWVXWO/events.json","paper":"https://pith.science/paper/DB7SDKZH"},"agent_actions":{"view_html":"https://pith.science/pith/DB7SDKZH63MF2IV6W37JTWVXWO","download_json":"https://pith.science/pith/DB7SDKZH63MF2IV6W37JTWVXWO.json","view_paper":"https://pith.science/paper/DB7SDKZH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.18558&json=true","fetch_graph":"https://pith.science/api/pith-number/DB7SDKZH63MF2IV6W37JTWVXWO/graph.json","fetch_events":"https://pith.science/api/pith-number/DB7SDKZH63MF2IV6W37JTWVXWO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DB7SDKZH63MF2IV6W37JTWVXWO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DB7SDKZH63MF2IV6W37JTWVXWO/action/storage_attestation","attest_author":"https://pith.science/pith/DB7SDKZH63MF2IV6W37JTWVXWO/action/author_attestation","sign_citation":"https://pith.science/pith/DB7SDKZH63MF2IV6W37JTWVXWO/action/citation_signature","submit_replication":"https://pith.science/pith/DB7SDKZH63MF2IV6W37JTWVXWO/action/replication_record"}},"created_at":"2026-07-05T08:11:58.164221+00:00","updated_at":"2026-07-05T08:11:58.164221+00:00"}