{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:54MYA25OMWTPVQXBCW4FIEXI6U","short_pith_number":"pith:54MYA25O","schema_version":"1.0","canonical_sha256":"ef19806bae65a6fac2e115b85412e8f5340e3afe4ca555ca107956aca55ac49c","source":{"kind":"arxiv","id":"1705.01196","version":2},"attestation_state":"computed","paper":{"title":"Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.AI","authors_text":"Akansel Cosgun, David Isele, Kaushik Subramanian, Kikuo Fujimura, Reza Rahimi","submitted_at":"2017-05-02T22:57:36Z","abstract_excerpt":"Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the c"},"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":"1705.01196","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-05-02T22:57:36Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"db76fdd8861b21e4ed7b216cfc1c076db044c0d452425714d6c4cbff2a6dc8e6","abstract_canon_sha256":"e6295f2540dd4a30f5facf54fe979cff1ae5174dc45f725cadadec9eb500cbec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:33.381875Z","signature_b64":"jhO4mC/pcHChHxwbCq6mX/7Tmp3Kv+z67KrgDvqdQV0ZeijAixJfqN8hWeEIFvkEZxGjiLi8JbVjG+LvRiBJBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef19806bae65a6fac2e115b85412e8f5340e3afe4ca555ca107956aca55ac49c","last_reissued_at":"2026-05-18T00:22:33.381204Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:33.381204Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.AI","authors_text":"Akansel Cosgun, David Isele, Kaushik Subramanian, Kikuo Fujimura, Reza Rahimi","submitted_at":"2017-05-02T22:57:36Z","abstract_excerpt":"Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.01196","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":"1705.01196","created_at":"2026-05-18T00:22:33.381302+00:00"},{"alias_kind":"arxiv_version","alias_value":"1705.01196v2","created_at":"2026-05-18T00:22:33.381302+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.01196","created_at":"2026-05-18T00:22:33.381302+00:00"},{"alias_kind":"pith_short_12","alias_value":"54MYA25OMWTP","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_16","alias_value":"54MYA25OMWTPVQXB","created_at":"2026-05-18T12:31:00.734936+00:00"},{"alias_kind":"pith_short_8","alias_value":"54MYA25O","created_at":"2026-05-18T12:31:00.734936+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/54MYA25OMWTPVQXBCW4FIEXI6U","json":"https://pith.science/pith/54MYA25OMWTPVQXBCW4FIEXI6U.json","graph_json":"https://pith.science/api/pith-number/54MYA25OMWTPVQXBCW4FIEXI6U/graph.json","events_json":"https://pith.science/api/pith-number/54MYA25OMWTPVQXBCW4FIEXI6U/events.json","paper":"https://pith.science/paper/54MYA25O"},"agent_actions":{"view_html":"https://pith.science/pith/54MYA25OMWTPVQXBCW4FIEXI6U","download_json":"https://pith.science/pith/54MYA25OMWTPVQXBCW4FIEXI6U.json","view_paper":"https://pith.science/paper/54MYA25O","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1705.01196&json=true","fetch_graph":"https://pith.science/api/pith-number/54MYA25OMWTPVQXBCW4FIEXI6U/graph.json","fetch_events":"https://pith.science/api/pith-number/54MYA25OMWTPVQXBCW4FIEXI6U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/54MYA25OMWTPVQXBCW4FIEXI6U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/54MYA25OMWTPVQXBCW4FIEXI6U/action/storage_attestation","attest_author":"https://pith.science/pith/54MYA25OMWTPVQXBCW4FIEXI6U/action/author_attestation","sign_citation":"https://pith.science/pith/54MYA25OMWTPVQXBCW4FIEXI6U/action/citation_signature","submit_replication":"https://pith.science/pith/54MYA25OMWTPVQXBCW4FIEXI6U/action/replication_record"}},"created_at":"2026-05-18T00:22:33.381302+00:00","updated_at":"2026-05-18T00:22:33.381302+00:00"}