{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:TPM3UCDDPIU6P4ZIS2RDI7AY3A","short_pith_number":"pith:TPM3UCDD","schema_version":"1.0","canonical_sha256":"9bd9ba08637a29e7f32896a2347c18d808d52add7e257102528007b818252228","source":{"kind":"arxiv","id":"1912.10773","version":1},"attestation_state":"computed","paper":{"title":"A Survey of Deep Learning Applications to Autonomous Vehicle Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.SY","eess.SY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Phil Barber, Richard Bowden, Saber Fallah, Sampo Kuutti, Yaochu Jin","submitted_at":"2019-12-23T12:50:32Z","abstract_excerpt":"Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although import"},"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":"1912.10773","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-12-23T12:50:32Z","cross_cats_sorted":["cs.CV","cs.SY","eess.SY","stat.ML"],"title_canon_sha256":"f0aef36f9ff3b4248a83960d77cb4872f5c14bae464e91f3ed71a69677711dbd","abstract_canon_sha256":"a404f28ab7b4e98d75c2b014aa61a34bfc68ff1f29bd21c31249b839eaa17547"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:28:02.399186Z","signature_b64":"a4j04sUVSMfQm7kG3X/DMnKaBJze0NoSwMD01GBlx3dddyGtvcWLWEWLXTCb8NiLN5gLk7myFqmSzeP6WkvFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9bd9ba08637a29e7f32896a2347c18d808d52add7e257102528007b818252228","last_reissued_at":"2026-07-05T00:28:02.398742Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:28:02.398742Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey of Deep Learning Applications to Autonomous Vehicle Control","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.SY","eess.SY","stat.ML"],"primary_cat":"cs.LG","authors_text":"Phil Barber, Richard Bowden, Saber Fallah, Sampo Kuutti, Yaochu Jin","submitted_at":"2019-12-23T12:50:32Z","abstract_excerpt":"Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although import"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.10773","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/1912.10773/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":"1912.10773","created_at":"2026-07-05T00:28:02.398807+00:00"},{"alias_kind":"arxiv_version","alias_value":"1912.10773v1","created_at":"2026-07-05T00:28:02.398807+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.10773","created_at":"2026-07-05T00:28:02.398807+00:00"},{"alias_kind":"pith_short_12","alias_value":"TPM3UCDDPIU6","created_at":"2026-07-05T00:28:02.398807+00:00"},{"alias_kind":"pith_short_16","alias_value":"TPM3UCDDPIU6P4ZI","created_at":"2026-07-05T00:28:02.398807+00:00"},{"alias_kind":"pith_short_8","alias_value":"TPM3UCDD","created_at":"2026-07-05T00:28:02.398807+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.11824","citing_title":"REFNet++: Multi-Task Efficient Fusion of Camera and Radar Sensor Data in Bird's-Eye Polar View","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TPM3UCDDPIU6P4ZIS2RDI7AY3A","json":"https://pith.science/pith/TPM3UCDDPIU6P4ZIS2RDI7AY3A.json","graph_json":"https://pith.science/api/pith-number/TPM3UCDDPIU6P4ZIS2RDI7AY3A/graph.json","events_json":"https://pith.science/api/pith-number/TPM3UCDDPIU6P4ZIS2RDI7AY3A/events.json","paper":"https://pith.science/paper/TPM3UCDD"},"agent_actions":{"view_html":"https://pith.science/pith/TPM3UCDDPIU6P4ZIS2RDI7AY3A","download_json":"https://pith.science/pith/TPM3UCDDPIU6P4ZIS2RDI7AY3A.json","view_paper":"https://pith.science/paper/TPM3UCDD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1912.10773&json=true","fetch_graph":"https://pith.science/api/pith-number/TPM3UCDDPIU6P4ZIS2RDI7AY3A/graph.json","fetch_events":"https://pith.science/api/pith-number/TPM3UCDDPIU6P4ZIS2RDI7AY3A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TPM3UCDDPIU6P4ZIS2RDI7AY3A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TPM3UCDDPIU6P4ZIS2RDI7AY3A/action/storage_attestation","attest_author":"https://pith.science/pith/TPM3UCDDPIU6P4ZIS2RDI7AY3A/action/author_attestation","sign_citation":"https://pith.science/pith/TPM3UCDDPIU6P4ZIS2RDI7AY3A/action/citation_signature","submit_replication":"https://pith.science/pith/TPM3UCDDPIU6P4ZIS2RDI7AY3A/action/replication_record"}},"created_at":"2026-07-05T00:28:02.398807+00:00","updated_at":"2026-07-05T00:28:02.398807+00:00"}