{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:XM7QARV2XJRCT2YC7ROSPS3PDW","short_pith_number":"pith:XM7QARV2","schema_version":"1.0","canonical_sha256":"bb3f0046baba6229eb02fc5d27cb6f1da3182fd2ea24549eef5cbab7941ffbc6","source":{"kind":"arxiv","id":"1910.07738","version":2},"attestation_state":"computed","paper":{"title":"A Survey of Deep Learning Techniques for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Bogdan Trasnea, Gigel Macesanu, Sorin Grigorescu, Tiberiu Cocias","submitted_at":"2019-10-17T07:05:28Z","abstract_excerpt":"The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorit"},"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":"1910.07738","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-17T07:05:28Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"9c94b85a500b175497999f4147cb3878f638a94dba81743843aad1f4eba46ebe","abstract_canon_sha256":"2deebce7dee5374cb88ef248719af76b8a304b1b1e6d9f2433c3a1c388c11905"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:50:25.964239Z","signature_b64":"sji+amc9Fdeo9nU3lnZq1yY2vSijNG1VyRrczc7oofSuHKJXxsr3R/ch+RP7K/OeVjkO7S4mk4Yw1pfLp8DjAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb3f0046baba6229eb02fc5d27cb6f1da3182fd2ea24549eef5cbab7941ffbc6","last_reissued_at":"2026-07-05T00:50:25.963848Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:50:25.963848Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Survey of Deep Learning Techniques for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Bogdan Trasnea, Gigel Macesanu, Sorin Grigorescu, Tiberiu Cocias","submitted_at":"2019-10-17T07:05:28Z","abstract_excerpt":"The last decade witnessed increasingly rapid progress in self-driving vehicle technology, mainly backed up by advances in the area of deep learning and artificial intelligence. The objective of this paper is to survey the current state-of-the-art on deep learning technologies used in autonomous driving. We start by presenting AI-based self-driving architectures, convolutional and recurrent neural networks, as well as the deep reinforcement learning paradigm. These methodologies form a base for the surveyed driving scene perception, path planning, behavior arbitration and motion control algorit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.07738","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/1910.07738/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":"1910.07738","created_at":"2026-07-05T00:50:25.963907+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.07738v2","created_at":"2026-07-05T00:50:25.963907+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.07738","created_at":"2026-07-05T00:50:25.963907+00:00"},{"alias_kind":"pith_short_12","alias_value":"XM7QARV2XJRC","created_at":"2026-07-05T00:50:25.963907+00:00"},{"alias_kind":"pith_short_16","alias_value":"XM7QARV2XJRCT2YC","created_at":"2026-07-05T00:50:25.963907+00:00"},{"alias_kind":"pith_short_8","alias_value":"XM7QARV2","created_at":"2026-07-05T00:50:25.963907+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.05526","citing_title":"Towards Learning Scalable Agile Dynamic Motion Planning for Robosoccer Teams with Policy Optimization","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XM7QARV2XJRCT2YC7ROSPS3PDW","json":"https://pith.science/pith/XM7QARV2XJRCT2YC7ROSPS3PDW.json","graph_json":"https://pith.science/api/pith-number/XM7QARV2XJRCT2YC7ROSPS3PDW/graph.json","events_json":"https://pith.science/api/pith-number/XM7QARV2XJRCT2YC7ROSPS3PDW/events.json","paper":"https://pith.science/paper/XM7QARV2"},"agent_actions":{"view_html":"https://pith.science/pith/XM7QARV2XJRCT2YC7ROSPS3PDW","download_json":"https://pith.science/pith/XM7QARV2XJRCT2YC7ROSPS3PDW.json","view_paper":"https://pith.science/paper/XM7QARV2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.07738&json=true","fetch_graph":"https://pith.science/api/pith-number/XM7QARV2XJRCT2YC7ROSPS3PDW/graph.json","fetch_events":"https://pith.science/api/pith-number/XM7QARV2XJRCT2YC7ROSPS3PDW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XM7QARV2XJRCT2YC7ROSPS3PDW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XM7QARV2XJRCT2YC7ROSPS3PDW/action/storage_attestation","attest_author":"https://pith.science/pith/XM7QARV2XJRCT2YC7ROSPS3PDW/action/author_attestation","sign_citation":"https://pith.science/pith/XM7QARV2XJRCT2YC7ROSPS3PDW/action/citation_signature","submit_replication":"https://pith.science/pith/XM7QARV2XJRCT2YC7ROSPS3PDW/action/replication_record"}},"created_at":"2026-07-05T00:50:25.963907+00:00","updated_at":"2026-07-05T00:50:25.963907+00:00"}