{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:5FSQ7CK3BT7KGANZD2F7IUQUXS","short_pith_number":"pith:5FSQ7CK3","canonical_record":{"source":{"id":"1808.04450","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-10T12:35:35Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"fe417cfc7e814ef4ed5d2e6b7e3254fe43527a64bb7a44f5a781a88bcc7b0786","abstract_canon_sha256":"bd205c773efacfb5a99f13e20b1815b8a52f8e5902694c9a2da1354f9e299889"},"schema_version":"1.0"},"canonical_sha256":"e9650f895b0cfea301b91e8bf45214bc94d5ee820a0ef5ed4a4646c3a364ab5a","source":{"kind":"arxiv","id":"1808.04450","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.04450","created_at":"2026-05-17T23:51:58Z"},{"alias_kind":"arxiv_version","alias_value":"1808.04450v2","created_at":"2026-05-17T23:51:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04450","created_at":"2026-05-17T23:51:58Z"},{"alias_kind":"pith_short_12","alias_value":"5FSQ7CK3BT7K","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5FSQ7CK3BT7KGANZ","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5FSQ7CK3","created_at":"2026-05-18T12:32:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:5FSQ7CK3BT7KGANZD2F7IUQUXS","target":"record","payload":{"canonical_record":{"source":{"id":"1808.04450","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-10T12:35:35Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"fe417cfc7e814ef4ed5d2e6b7e3254fe43527a64bb7a44f5a781a88bcc7b0786","abstract_canon_sha256":"bd205c773efacfb5a99f13e20b1815b8a52f8e5902694c9a2da1354f9e299889"},"schema_version":"1.0"},"canonical_sha256":"e9650f895b0cfea301b91e8bf45214bc94d5ee820a0ef5ed4a4646c3a364ab5a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:58.901535Z","signature_b64":"ZxQ3PdgdCRJNO5tIjx6OoaZbiUagOW6Yy1II6LLhmyzCP5ZJFPAR3jh8W3bu6RB5q5o1Iv3a0vZo5ZEj/6oYBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e9650f895b0cfea301b91e8bf45214bc94d5ee820a0ef5ed4a4646c3a364ab5a","last_reissued_at":"2026-05-17T23:51:58.901080Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:58.901080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1808.04450","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:51:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fVBob+Z6sCpZEWmvSlZpQ9N3m0nmGqUg7KZuAlIUjlpkwoHZsH17yaedepLe8BM5OlEFfqiYDsxtvDjVHK8eCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:49:57.784127Z"},"content_sha256":"95c352c475f6713cabb562093aeb81eb7952060ca30ccf8715046b2e69adb352","schema_version":"1.0","event_id":"sha256:95c352c475f6713cabb562093aeb81eb7952060ca30ccf8715046b2e69adb352"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:5FSQ7CK3BT7KGANZD2F7IUQUXS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Road Segmentation Using CNN and Distributed LSTM","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Lin Bai, Xinming Huang, Yecheng Lyu","submitted_at":"2018-08-10T12:35:35Z","abstract_excerpt":"In automated driving systems (ADS) and advanced driver-assistance systems (ADAS), an efficient road segmentation is necessary to perceive the drivable region and build an occupancy map for path planning. The existing algorithms implement gigantic convolutional neural networks (CNNs) that are computationally expensive and time consuming. In this paper, we introduced distributed LSTM, a neural network widely used in audio and video processing, to process rows and columns in images and feature maps. We then propose a new network combining the convolutional and distributed LSTM layers to solve the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04450","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:51:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"guqVGoUEFsNIhKriutovS8xBwPzee3jNOeMTV0znW0iMAWN42uAEWioW9ibisJS4WAwIlu3WWEHqBg24IMTTAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T02:49:57.784533Z"},"content_sha256":"e2b4c44c0e9818bcfee3bb4da69fdfcb77a50fe4136aa2c55293449acb67609f","schema_version":"1.0","event_id":"sha256:e2b4c44c0e9818bcfee3bb4da69fdfcb77a50fe4136aa2c55293449acb67609f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5FSQ7CK3BT7KGANZD2F7IUQUXS/bundle.json","state_url":"https://pith.science/pith/5FSQ7CK3BT7KGANZD2F7IUQUXS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5FSQ7CK3BT7KGANZD2F7IUQUXS/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":"2026-05-26T02:49:57Z","links":{"resolver":"https://pith.science/pith/5FSQ7CK3BT7KGANZD2F7IUQUXS","bundle":"https://pith.science/pith/5FSQ7CK3BT7KGANZD2F7IUQUXS/bundle.json","state":"https://pith.science/pith/5FSQ7CK3BT7KGANZD2F7IUQUXS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5FSQ7CK3BT7KGANZD2F7IUQUXS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:5FSQ7CK3BT7KGANZD2F7IUQUXS","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":"bd205c773efacfb5a99f13e20b1815b8a52f8e5902694c9a2da1354f9e299889","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-10T12:35:35Z","title_canon_sha256":"fe417cfc7e814ef4ed5d2e6b7e3254fe43527a64bb7a44f5a781a88bcc7b0786"},"schema_version":"1.0","source":{"id":"1808.04450","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1808.04450","created_at":"2026-05-17T23:51:58Z"},{"alias_kind":"arxiv_version","alias_value":"1808.04450v2","created_at":"2026-05-17T23:51:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04450","created_at":"2026-05-17T23:51:58Z"},{"alias_kind":"pith_short_12","alias_value":"5FSQ7CK3BT7K","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_16","alias_value":"5FSQ7CK3BT7KGANZ","created_at":"2026-05-18T12:32:08Z"},{"alias_kind":"pith_short_8","alias_value":"5FSQ7CK3","created_at":"2026-05-18T12:32:08Z"}],"graph_snapshots":[{"event_id":"sha256:e2b4c44c0e9818bcfee3bb4da69fdfcb77a50fe4136aa2c55293449acb67609f","target":"graph","created_at":"2026-05-17T23:51:58Z","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":"In automated driving systems (ADS) and advanced driver-assistance systems (ADAS), an efficient road segmentation is necessary to perceive the drivable region and build an occupancy map for path planning. The existing algorithms implement gigantic convolutional neural networks (CNNs) that are computationally expensive and time consuming. In this paper, we introduced distributed LSTM, a neural network widely used in audio and video processing, to process rows and columns in images and feature maps. We then propose a new network combining the convolutional and distributed LSTM layers to solve the","authors_text":"Lin Bai, Xinming Huang, Yecheng Lyu","cross_cats":["eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-10T12:35:35Z","title":"Road Segmentation Using CNN and Distributed LSTM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04450","kind":"arxiv","version":2},"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":"sha256:95c352c475f6713cabb562093aeb81eb7952060ca30ccf8715046b2e69adb352","target":"record","created_at":"2026-05-17T23:51:58Z","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":"bd205c773efacfb5a99f13e20b1815b8a52f8e5902694c9a2da1354f9e299889","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-08-10T12:35:35Z","title_canon_sha256":"fe417cfc7e814ef4ed5d2e6b7e3254fe43527a64bb7a44f5a781a88bcc7b0786"},"schema_version":"1.0","source":{"id":"1808.04450","kind":"arxiv","version":2}},"canonical_sha256":"e9650f895b0cfea301b91e8bf45214bc94d5ee820a0ef5ed4a4646c3a364ab5a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e9650f895b0cfea301b91e8bf45214bc94d5ee820a0ef5ed4a4646c3a364ab5a","first_computed_at":"2026-05-17T23:51:58.901080Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:58.901080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZxQ3PdgdCRJNO5tIjx6OoaZbiUagOW6Yy1II6LLhmyzCP5ZJFPAR3jh8W3bu6RB5q5o1Iv3a0vZo5ZEj/6oYBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:58.901535Z","signed_message":"canonical_sha256_bytes"},"source_id":"1808.04450","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:95c352c475f6713cabb562093aeb81eb7952060ca30ccf8715046b2e69adb352","sha256:e2b4c44c0e9818bcfee3bb4da69fdfcb77a50fe4136aa2c55293449acb67609f"],"state_sha256":"78d38a33cc697210fb05dba508675b83b614d7fb1418297a59566ffdcab7cf8b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+PodVePSKYYIih7T938vMA7/pBvW+HcHqoK319NqDZyn8hgLZJHqqWJWxsU9f/Frb6k832KUltO22MiYOhCKAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T02:49:57.787961Z","bundle_sha256":"8a6c1c953dd4885a649fbe943896008f59ba10bfd2555d3dc7331316c4ef96b6"}}