{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NBX6BMNKDDARNT7IVX2VMPM4TJ","short_pith_number":"pith:NBX6BMNK","schema_version":"1.0","canonical_sha256":"686fe0b1aa18c116cfe8adf5563d9c9a4a4c6ebab00d28b1d7a00fb65d09c2f1","source":{"kind":"arxiv","id":"2305.19904","version":2},"attestation_state":"computed","paper":{"title":"Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Abhinav Valada, Marco Caccamo, Martin B\\\"uchner, Raphael Trumpp","submitted_at":"2023-05-31T14:38:00Z","abstract_excerpt":"Autonomous driving involves complex decision-making in highly interactive environments, requiring thoughtful negotiation with other traffic participants. While reinforcement learning provides a way to learn such interaction behavior, efficient learning critically depends on scalable state representations. Contrary to imitation learning methods, high-dimensional state representations still constitute a major bottleneck for deep reinforcement learning methods in autonomous driving. In this paper, we study the challenges of constructing bird's-eye-view representations for autonomous driving and p"},"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":"2305.19904","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-05-31T14:38:00Z","cross_cats_sorted":[],"title_canon_sha256":"d79309f4e1ab6af83a57a8fab29391d40618fd51f9494621f1b459c23287d6d7","abstract_canon_sha256":"7c1cfaff37d9d80ce013a879d93476fde50b1c9b888b2c642bb84613359a7fe1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:41:19.153727Z","signature_b64":"JsRUNx2O+xH+IBrJ7YpC+Q8x6a75idKoQqZIUqulYvfenacrfMX4HOeuCJ/hrg7KLDCFlCpgC3BJlWkR8FvYBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"686fe0b1aa18c116cfe8adf5563d9c9a4a4c6ebab00d28b1d7a00fb65d09c2f1","last_reissued_at":"2026-07-05T06:41:19.153285Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:41:19.153285Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Abhinav Valada, Marco Caccamo, Martin B\\\"uchner, Raphael Trumpp","submitted_at":"2023-05-31T14:38:00Z","abstract_excerpt":"Autonomous driving involves complex decision-making in highly interactive environments, requiring thoughtful negotiation with other traffic participants. While reinforcement learning provides a way to learn such interaction behavior, efficient learning critically depends on scalable state representations. Contrary to imitation learning methods, high-dimensional state representations still constitute a major bottleneck for deep reinforcement learning methods in autonomous driving. In this paper, we study the challenges of constructing bird's-eye-view representations for autonomous driving and p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.19904","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/2305.19904/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":"2305.19904","created_at":"2026-07-05T06:41:19.153367+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.19904v2","created_at":"2026-07-05T06:41:19.153367+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.19904","created_at":"2026-07-05T06:41:19.153367+00:00"},{"alias_kind":"pith_short_12","alias_value":"NBX6BMNKDDAR","created_at":"2026-07-05T06:41:19.153367+00:00"},{"alias_kind":"pith_short_16","alias_value":"NBX6BMNKDDARNT7I","created_at":"2026-07-05T06:41:19.153367+00:00"},{"alias_kind":"pith_short_8","alias_value":"NBX6BMNK","created_at":"2026-07-05T06:41:19.153367+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.08221","citing_title":"A Comprehensive Review of Reinforcement Learning for Autonomous Driving in the CARLA Simulator","ref_index":47,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NBX6BMNKDDARNT7IVX2VMPM4TJ","json":"https://pith.science/pith/NBX6BMNKDDARNT7IVX2VMPM4TJ.json","graph_json":"https://pith.science/api/pith-number/NBX6BMNKDDARNT7IVX2VMPM4TJ/graph.json","events_json":"https://pith.science/api/pith-number/NBX6BMNKDDARNT7IVX2VMPM4TJ/events.json","paper":"https://pith.science/paper/NBX6BMNK"},"agent_actions":{"view_html":"https://pith.science/pith/NBX6BMNKDDARNT7IVX2VMPM4TJ","download_json":"https://pith.science/pith/NBX6BMNKDDARNT7IVX2VMPM4TJ.json","view_paper":"https://pith.science/paper/NBX6BMNK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.19904&json=true","fetch_graph":"https://pith.science/api/pith-number/NBX6BMNKDDARNT7IVX2VMPM4TJ/graph.json","fetch_events":"https://pith.science/api/pith-number/NBX6BMNKDDARNT7IVX2VMPM4TJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NBX6BMNKDDARNT7IVX2VMPM4TJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NBX6BMNKDDARNT7IVX2VMPM4TJ/action/storage_attestation","attest_author":"https://pith.science/pith/NBX6BMNKDDARNT7IVX2VMPM4TJ/action/author_attestation","sign_citation":"https://pith.science/pith/NBX6BMNKDDARNT7IVX2VMPM4TJ/action/citation_signature","submit_replication":"https://pith.science/pith/NBX6BMNKDDARNT7IVX2VMPM4TJ/action/replication_record"}},"created_at":"2026-07-05T06:41:19.153367+00:00","updated_at":"2026-07-05T06:41:19.153367+00:00"}