{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:VSP2NWBBS6SLVEXTR5VHZHP5GF","short_pith_number":"pith:VSP2NWBB","schema_version":"1.0","canonical_sha256":"ac9fa6d82197a4ba92f38f6a7c9dfd3160e9e3a4645e5ca6227b55ded1ae07da","source":{"kind":"arxiv","id":"2306.08865","version":1},"attestation_state":"computed","paper":{"title":"One-Shot Learning of Visual Path Navigation for Autonomous Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Amin Ghafourian, Anjali Krishnamachar, Debo Shi, Francois Charette, Iman Soltani, Matthew Cui, Zhongying CuiZhu","submitted_at":"2023-06-15T05:27:46Z","abstract_excerpt":"Autonomous driving presents many challenges due to the large number of scenarios the autonomous vehicle (AV) may encounter. End-to-end deep learning models are comparatively simplistic models that can handle a broad set of scenarios. However, end-to-end models require large amounts of diverse data to perform well. This paper presents a novel deep neural network that performs image-to-steering path navigation that helps with the data problem by adding one-shot learning to the system. Presented with a previously unseen path, the vehicle can drive the path autonomously after being shown the path "},"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":"2306.08865","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-06-15T05:27:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"33c6c6e1d13ef3b2017a68cbfdf33ebcb4e8739fd2f0f93f5a445971bb3c10b2","abstract_canon_sha256":"4f4b1d3f28c0e70c53021392b7bb46a2228249d7201d31b927f224a89789010a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:20:57.272266Z","signature_b64":"YQvKSLyHdXo0sTmynNH5yO1raMIQ+Olbdxwajg0xIT2lEq6VwLT3a1+8UICupI+HfAiAWIKlmxAZ6GR3yy0zCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac9fa6d82197a4ba92f38f6a7c9dfd3160e9e3a4645e5ca6227b55ded1ae07da","last_reissued_at":"2026-07-05T06:20:57.271859Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:20:57.271859Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"One-Shot Learning of Visual Path Navigation for Autonomous Vehicles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Amin Ghafourian, Anjali Krishnamachar, Debo Shi, Francois Charette, Iman Soltani, Matthew Cui, Zhongying CuiZhu","submitted_at":"2023-06-15T05:27:46Z","abstract_excerpt":"Autonomous driving presents many challenges due to the large number of scenarios the autonomous vehicle (AV) may encounter. End-to-end deep learning models are comparatively simplistic models that can handle a broad set of scenarios. However, end-to-end models require large amounts of diverse data to perform well. This paper presents a novel deep neural network that performs image-to-steering path navigation that helps with the data problem by adding one-shot learning to the system. Presented with a previously unseen path, the vehicle can drive the path autonomously after being shown the path "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.08865","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/2306.08865/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":"2306.08865","created_at":"2026-07-05T06:20:57.271915+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.08865v1","created_at":"2026-07-05T06:20:57.271915+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.08865","created_at":"2026-07-05T06:20:57.271915+00:00"},{"alias_kind":"pith_short_12","alias_value":"VSP2NWBBS6SL","created_at":"2026-07-05T06:20:57.271915+00:00"},{"alias_kind":"pith_short_16","alias_value":"VSP2NWBBS6SLVEXT","created_at":"2026-07-05T06:20:57.271915+00:00"},{"alias_kind":"pith_short_8","alias_value":"VSP2NWBB","created_at":"2026-07-05T06:20:57.271915+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.12800","citing_title":"FFI-VTR: Lightweight and Robust Visual Teach and Repeat Navigation based on Feature Flow Indicator and Probabilistic Motion Planning","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VSP2NWBBS6SLVEXTR5VHZHP5GF","json":"https://pith.science/pith/VSP2NWBBS6SLVEXTR5VHZHP5GF.json","graph_json":"https://pith.science/api/pith-number/VSP2NWBBS6SLVEXTR5VHZHP5GF/graph.json","events_json":"https://pith.science/api/pith-number/VSP2NWBBS6SLVEXTR5VHZHP5GF/events.json","paper":"https://pith.science/paper/VSP2NWBB"},"agent_actions":{"view_html":"https://pith.science/pith/VSP2NWBBS6SLVEXTR5VHZHP5GF","download_json":"https://pith.science/pith/VSP2NWBBS6SLVEXTR5VHZHP5GF.json","view_paper":"https://pith.science/paper/VSP2NWBB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.08865&json=true","fetch_graph":"https://pith.science/api/pith-number/VSP2NWBBS6SLVEXTR5VHZHP5GF/graph.json","fetch_events":"https://pith.science/api/pith-number/VSP2NWBBS6SLVEXTR5VHZHP5GF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VSP2NWBBS6SLVEXTR5VHZHP5GF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VSP2NWBBS6SLVEXTR5VHZHP5GF/action/storage_attestation","attest_author":"https://pith.science/pith/VSP2NWBBS6SLVEXTR5VHZHP5GF/action/author_attestation","sign_citation":"https://pith.science/pith/VSP2NWBBS6SLVEXTR5VHZHP5GF/action/citation_signature","submit_replication":"https://pith.science/pith/VSP2NWBBS6SLVEXTR5VHZHP5GF/action/replication_record"}},"created_at":"2026-07-05T06:20:57.271915+00:00","updated_at":"2026-07-05T06:20:57.271915+00:00"}