{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:KJJLITRRESZZDYAP24RFUGX637","short_pith_number":"pith:KJJLITRR","schema_version":"1.0","canonical_sha256":"5252b44e3124b391e00fd7225a1afedfdccfb70a6f380967d73ef790dda906c0","source":{"kind":"arxiv","id":"1612.04340","version":1},"attestation_state":"computed","paper":{"title":"End-to-End Deep Reinforcement Learning for Lane Keeping Assist","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"stat.ML","authors_text":"Ahmad El Sallab, Etienne Perot, Mohammed Abdou, Senthil Yogamani","submitted_at":"2016-12-13T20:19:42Z","abstract_excerpt":"Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment. Motivated by Google DeepMind's successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement lea"},"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":"1612.04340","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2016-12-13T20:19:42Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"b1f5e8016c1adeba4811b6f667290ba67d57bcd6b2210d623d66493025488d80","abstract_canon_sha256":"7fdd2ebad04cdf7da103ed97b31cfb18d1b0152f9bd55a86f6f27241cabefc34"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:55:04.794448Z","signature_b64":"E6M1X89bfNAtzHCW8VCqSOgF+Vykq82c3jz9v2ue6bU1bAukwBJfG/JASQ3jhcA1XbkaZhl2Ucfth5QolJ5mBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5252b44e3124b391e00fd7225a1afedfdccfb70a6f380967d73ef790dda906c0","last_reissued_at":"2026-05-18T00:55:04.793910Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:55:04.793910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End-to-End Deep Reinforcement Learning for Lane Keeping Assist","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"stat.ML","authors_text":"Ahmad El Sallab, Etienne Perot, Mohammed Abdou, Senthil Yogamani","submitted_at":"2016-12-13T20:19:42Z","abstract_excerpt":"Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes, but it has not yet been successfully used for automotive applications. There has recently been a revival of interest in the topic, however, driven by the ability of deep learning algorithms to learn good representations of the environment. Motivated by Google DeepMind's successful demonstrations of learning for games from Breakout to Go, we will propose different methods for autonomous driving using deep reinforcement lea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.04340","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":""},"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":"1612.04340","created_at":"2026-05-18T00:55:04.793990+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.04340v1","created_at":"2026-05-18T00:55:04.793990+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.04340","created_at":"2026-05-18T00:55:04.793990+00:00"},{"alias_kind":"pith_short_12","alias_value":"KJJLITRRESZZ","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_16","alias_value":"KJJLITRRESZZDYAP","created_at":"2026-05-18T12:30:25.849896+00:00"},{"alias_kind":"pith_short_8","alias_value":"KJJLITRR","created_at":"2026-05-18T12:30:25.849896+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1906.08662","citing_title":"Cooperative Lane Changing via Deep Reinforcement Learning","ref_index":2,"is_internal_anchor":true},{"citing_arxiv_id":"2605.10177","citing_title":"MTA-RL: Robust Urban Driving via Multi-modal Transformer-based 3D Affordances and Reinforcement Learning","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KJJLITRRESZZDYAP24RFUGX637","json":"https://pith.science/pith/KJJLITRRESZZDYAP24RFUGX637.json","graph_json":"https://pith.science/api/pith-number/KJJLITRRESZZDYAP24RFUGX637/graph.json","events_json":"https://pith.science/api/pith-number/KJJLITRRESZZDYAP24RFUGX637/events.json","paper":"https://pith.science/paper/KJJLITRR"},"agent_actions":{"view_html":"https://pith.science/pith/KJJLITRRESZZDYAP24RFUGX637","download_json":"https://pith.science/pith/KJJLITRRESZZDYAP24RFUGX637.json","view_paper":"https://pith.science/paper/KJJLITRR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.04340&json=true","fetch_graph":"https://pith.science/api/pith-number/KJJLITRRESZZDYAP24RFUGX637/graph.json","fetch_events":"https://pith.science/api/pith-number/KJJLITRRESZZDYAP24RFUGX637/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KJJLITRRESZZDYAP24RFUGX637/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KJJLITRRESZZDYAP24RFUGX637/action/storage_attestation","attest_author":"https://pith.science/pith/KJJLITRRESZZDYAP24RFUGX637/action/author_attestation","sign_citation":"https://pith.science/pith/KJJLITRRESZZDYAP24RFUGX637/action/citation_signature","submit_replication":"https://pith.science/pith/KJJLITRRESZZDYAP24RFUGX637/action/replication_record"}},"created_at":"2026-05-18T00:55:04.793990+00:00","updated_at":"2026-05-18T00:55:04.793990+00:00"}