{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:U22IXOLQJHIS2C6XAOKXYCAXRZ","short_pith_number":"pith:U22IXOLQ","canonical_record":{"source":{"id":"1806.06498","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-18T04:31:46Z","cross_cats_sorted":["cs.LG","cs.SY"],"title_canon_sha256":"aeb316f1a427ee76be854cabac2b73d41273a7d8bcf55a310b8188110f57e1c9","abstract_canon_sha256":"f03980a2f232cdbca0c5f0c6a2fe64ef85fbe4daafc90acb7ba7155639453067"},"schema_version":"1.0"},"canonical_sha256":"a6b48bb97049d12d0bd703957c08178e48478d09e865ad032f94018338330e46","source":{"kind":"arxiv","id":"1806.06498","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.06498","created_at":"2026-05-18T00:01:38Z"},{"alias_kind":"arxiv_version","alias_value":"1806.06498v3","created_at":"2026-05-18T00:01:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.06498","created_at":"2026-05-18T00:01:38Z"},{"alias_kind":"pith_short_12","alias_value":"U22IXOLQJHIS","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"U22IXOLQJHIS2C6X","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"U22IXOLQ","created_at":"2026-05-18T12:32:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:U22IXOLQJHIS2C6XAOKXYCAXRZ","target":"record","payload":{"canonical_record":{"source":{"id":"1806.06498","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-18T04:31:46Z","cross_cats_sorted":["cs.LG","cs.SY"],"title_canon_sha256":"aeb316f1a427ee76be854cabac2b73d41273a7d8bcf55a310b8188110f57e1c9","abstract_canon_sha256":"f03980a2f232cdbca0c5f0c6a2fe64ef85fbe4daafc90acb7ba7155639453067"},"schema_version":"1.0"},"canonical_sha256":"a6b48bb97049d12d0bd703957c08178e48478d09e865ad032f94018338330e46","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:38.185392Z","signature_b64":"OI5Rqw1U1+BD7IurXG8NrZ0DYCRgYFRCNx/Z9wvnFxU3+UrtJ8Iqm+pXXSUO01+O7Rs0CcPXjf3pKdP0HgB9Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6b48bb97049d12d0bd703957c08178e48478d09e865ad032f94018338330e46","last_reissued_at":"2026-05-18T00:01:38.184715Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:38.184715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.06498","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SqjdJb5zZyVVnbcedCjKInxP+gjW3ee/yLHRSHNfeHNejkP2pgLCTkNzZ8/NA8XC/gNJ3Is9Jn5qCIIlnEIDBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:38:49.851959Z"},"content_sha256":"2ee3a1becc557d3241d79ffc8034151224726f474b9c2b222de090b3e02d6e43","schema_version":"1.0","event_id":"sha256:2ee3a1becc557d3241d79ffc8034151224726f474b9c2b222de090b3e02d6e43"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:U22IXOLQJHIS2C6XAOKXYCAXRZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Conditional Affordance Learning for Driving in Urban Environments","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"cs.RO","authors_text":"Andreas Geiger, Axel Sauer, Nikolay Savinov","submitted_at":"2018-06-18T04:31:46Z","abstract_excerpt":"Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of both by using a neural network to learn appropriate low-dimensional intermediate representations. However, existing direct perception approaches are restricted to simple highway situations, lacking the ability to navigate intersections, stop at traffic lights or respect speed limit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.06498","kind":"arxiv","version":3},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:01:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CB+I2Uc29G1TJOyPZh6cD2LBIBBaYWIB9VIYDM40Um/0u4h2PJIAcF+JQ5dnSzt4tkhDrhixhu/7vISME+I8Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:38:49.852513Z"},"content_sha256":"71a6e784218f8090482cf59186dbd97b87b3e7985cc368ecad4ea7c526c6f146","schema_version":"1.0","event_id":"sha256:71a6e784218f8090482cf59186dbd97b87b3e7985cc368ecad4ea7c526c6f146"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U22IXOLQJHIS2C6XAOKXYCAXRZ/bundle.json","state_url":"https://pith.science/pith/U22IXOLQJHIS2C6XAOKXYCAXRZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U22IXOLQJHIS2C6XAOKXYCAXRZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-01T19:38:49Z","links":{"resolver":"https://pith.science/pith/U22IXOLQJHIS2C6XAOKXYCAXRZ","bundle":"https://pith.science/pith/U22IXOLQJHIS2C6XAOKXYCAXRZ/bundle.json","state":"https://pith.science/pith/U22IXOLQJHIS2C6XAOKXYCAXRZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U22IXOLQJHIS2C6XAOKXYCAXRZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:U22IXOLQJHIS2C6XAOKXYCAXRZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f03980a2f232cdbca0c5f0c6a2fe64ef85fbe4daafc90acb7ba7155639453067","cross_cats_sorted":["cs.LG","cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-18T04:31:46Z","title_canon_sha256":"aeb316f1a427ee76be854cabac2b73d41273a7d8bcf55a310b8188110f57e1c9"},"schema_version":"1.0","source":{"id":"1806.06498","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.06498","created_at":"2026-05-18T00:01:38Z"},{"alias_kind":"arxiv_version","alias_value":"1806.06498v3","created_at":"2026-05-18T00:01:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.06498","created_at":"2026-05-18T00:01:38Z"},{"alias_kind":"pith_short_12","alias_value":"U22IXOLQJHIS","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_16","alias_value":"U22IXOLQJHIS2C6X","created_at":"2026-05-18T12:32:56Z"},{"alias_kind":"pith_short_8","alias_value":"U22IXOLQ","created_at":"2026-05-18T12:32:56Z"}],"graph_snapshots":[{"event_id":"sha256:71a6e784218f8090482cf59186dbd97b87b3e7985cc368ecad4ea7c526c6f146","target":"graph","created_at":"2026-05-18T00:01:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Most existing approaches to autonomous driving fall into one of two categories: modular pipelines, that build an extensive model of the environment, and imitation learning approaches, that map images directly to control outputs. A recently proposed third paradigm, direct perception, aims to combine the advantages of both by using a neural network to learn appropriate low-dimensional intermediate representations. However, existing direct perception approaches are restricted to simple highway situations, lacking the ability to navigate intersections, stop at traffic lights or respect speed limit","authors_text":"Andreas Geiger, Axel Sauer, Nikolay Savinov","cross_cats":["cs.LG","cs.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-18T04:31:46Z","title":"Conditional Affordance Learning for Driving in Urban Environments"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.06498","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:2ee3a1becc557d3241d79ffc8034151224726f474b9c2b222de090b3e02d6e43","target":"record","created_at":"2026-05-18T00:01:38Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f03980a2f232cdbca0c5f0c6a2fe64ef85fbe4daafc90acb7ba7155639453067","cross_cats_sorted":["cs.LG","cs.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-06-18T04:31:46Z","title_canon_sha256":"aeb316f1a427ee76be854cabac2b73d41273a7d8bcf55a310b8188110f57e1c9"},"schema_version":"1.0","source":{"id":"1806.06498","kind":"arxiv","version":3}},"canonical_sha256":"a6b48bb97049d12d0bd703957c08178e48478d09e865ad032f94018338330e46","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a6b48bb97049d12d0bd703957c08178e48478d09e865ad032f94018338330e46","first_computed_at":"2026-05-18T00:01:38.184715Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:38.184715Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OI5Rqw1U1+BD7IurXG8NrZ0DYCRgYFRCNx/Z9wvnFxU3+UrtJ8Iqm+pXXSUO01+O7Rs0CcPXjf3pKdP0HgB9Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:38.185392Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.06498","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2ee3a1becc557d3241d79ffc8034151224726f474b9c2b222de090b3e02d6e43","sha256:71a6e784218f8090482cf59186dbd97b87b3e7985cc368ecad4ea7c526c6f146"],"state_sha256":"a0fb77b3cdc05c33abc6eb02c0fb4969d47d0e33c9eb2a116d4a176988e4bb48"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pwve78zux3ftLP+7ro4zkEVxLHIkBihAFUTS2l6AoA+HIFR0SpVjGPoTpwRBVCKSg5vinqAqqpAZzPBYl8oaAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T19:38:49.855229Z","bundle_sha256":"27248b1282f9126597d912fa0a2da848edcd5c165bc16569afc806ec5f10bd58"}}