{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:D5UBEDSCRRBWFGXTWDH2VDIGX5","short_pith_number":"pith:D5UBEDSC","schema_version":"1.0","canonical_sha256":"1f68120e428c43629af3b0cfaa8d06bf49771e9934db420ed59cb29621c0d6d4","source":{"kind":"arxiv","id":"1811.05785","version":2},"attestation_state":"computed","paper":{"title":"Two-stream convolutional networks for end-to-end learning of self-driving cars","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Nelson Fernandez","submitted_at":"2018-11-13T12:34:42Z","abstract_excerpt":"We propose a methodology to extend the concept of Two-Stream Convolutional Networks to perform end-to-end learning for self-driving cars with temporal cues. The system has the ability to learn spatiotemporal features by simultaneously mapping raw images and pre-calculated optical flows directly to steering commands. Although optical flows encode temporal-rich information, we found that 2D-CNNs are prone to capturing features only as spatial representations. We show how the use of Multitask Learning favors the learning of temporal features via inductive transfer from a shared spatiotemporal rep"},"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":"1811.05785","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-11-13T12:34:42Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"47ed0a5e4c0c44f46b70f65f15ea3d74a91a3ca768d1781a87654f94ff114739","abstract_canon_sha256":"e89a24e24532e0a2775da7bfaff0263cbecccc8579ca2529dd5097a248ec3278"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:13.451193Z","signature_b64":"8fEf7uYswjAUNeKZeZgyYskC9NLr5/u8/fhiMkFZKadWSgaEYd4DJdi39xTGCrZYOy0OBwdYrTd2KCjxP9ErCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f68120e428c43629af3b0cfaa8d06bf49771e9934db420ed59cb29621c0d6d4","last_reissued_at":"2026-05-17T23:58:13.450709Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:13.450709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Two-stream convolutional networks for end-to-end learning of self-driving cars","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Nelson Fernandez","submitted_at":"2018-11-13T12:34:42Z","abstract_excerpt":"We propose a methodology to extend the concept of Two-Stream Convolutional Networks to perform end-to-end learning for self-driving cars with temporal cues. The system has the ability to learn spatiotemporal features by simultaneously mapping raw images and pre-calculated optical flows directly to steering commands. Although optical flows encode temporal-rich information, we found that 2D-CNNs are prone to capturing features only as spatial representations. We show how the use of Multitask Learning favors the learning of temporal features via inductive transfer from a shared spatiotemporal rep"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.05785","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":""},"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":"1811.05785","created_at":"2026-05-17T23:58:13.450786+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.05785v2","created_at":"2026-05-17T23:58:13.450786+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.05785","created_at":"2026-05-17T23:58:13.450786+00:00"},{"alias_kind":"pith_short_12","alias_value":"D5UBEDSCRRBW","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_16","alias_value":"D5UBEDSCRRBWFGXT","created_at":"2026-05-18T12:32:19.392346+00:00"},{"alias_kind":"pith_short_8","alias_value":"D5UBEDSC","created_at":"2026-05-18T12:32:19.392346+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/D5UBEDSCRRBWFGXTWDH2VDIGX5","json":"https://pith.science/pith/D5UBEDSCRRBWFGXTWDH2VDIGX5.json","graph_json":"https://pith.science/api/pith-number/D5UBEDSCRRBWFGXTWDH2VDIGX5/graph.json","events_json":"https://pith.science/api/pith-number/D5UBEDSCRRBWFGXTWDH2VDIGX5/events.json","paper":"https://pith.science/paper/D5UBEDSC"},"agent_actions":{"view_html":"https://pith.science/pith/D5UBEDSCRRBWFGXTWDH2VDIGX5","download_json":"https://pith.science/pith/D5UBEDSCRRBWFGXTWDH2VDIGX5.json","view_paper":"https://pith.science/paper/D5UBEDSC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.05785&json=true","fetch_graph":"https://pith.science/api/pith-number/D5UBEDSCRRBWFGXTWDH2VDIGX5/graph.json","fetch_events":"https://pith.science/api/pith-number/D5UBEDSCRRBWFGXTWDH2VDIGX5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D5UBEDSCRRBWFGXTWDH2VDIGX5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D5UBEDSCRRBWFGXTWDH2VDIGX5/action/storage_attestation","attest_author":"https://pith.science/pith/D5UBEDSCRRBWFGXTWDH2VDIGX5/action/author_attestation","sign_citation":"https://pith.science/pith/D5UBEDSCRRBWFGXTWDH2VDIGX5/action/citation_signature","submit_replication":"https://pith.science/pith/D5UBEDSCRRBWFGXTWDH2VDIGX5/action/replication_record"}},"created_at":"2026-05-17T23:58:13.450786+00:00","updated_at":"2026-05-17T23:58:13.450786+00:00"}