{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:SZWZQAYFNX7KZQZJ4H52OCQY4C","short_pith_number":"pith:SZWZQAYF","canonical_record":{"source":{"id":"1812.01802","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-12-05T03:30:54Z","cross_cats_sorted":[],"title_canon_sha256":"7d10d2d468b5d85e0a14b3b9e3ba0c8521a71dc9232d95c854d4cec315f1ca08","abstract_canon_sha256":"ecec2a769d49e44b93f9b269a3cd903f6388614dc720b99107e2a3950c1d5189"},"schema_version":"1.0"},"canonical_sha256":"966d9803056dfeacc329e1fba70a18e0ba3eb505a73144adc438dd1835bc8a98","source":{"kind":"arxiv","id":"1812.01802","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.01802","created_at":"2026-05-17T23:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"1812.01802v1","created_at":"2026-05-17T23:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01802","created_at":"2026-05-17T23:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"SZWZQAYFNX7K","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SZWZQAYFNX7KZQZJ","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SZWZQAYF","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:SZWZQAYFNX7KZQZJ4H52OCQY4C","target":"record","payload":{"canonical_record":{"source":{"id":"1812.01802","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-12-05T03:30:54Z","cross_cats_sorted":[],"title_canon_sha256":"7d10d2d468b5d85e0a14b3b9e3ba0c8521a71dc9232d95c854d4cec315f1ca08","abstract_canon_sha256":"ecec2a769d49e44b93f9b269a3cd903f6388614dc720b99107e2a3950c1d5189"},"schema_version":"1.0"},"canonical_sha256":"966d9803056dfeacc329e1fba70a18e0ba3eb505a73144adc438dd1835bc8a98","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:00.582943Z","signature_b64":"WFnVt0bSK/veFQd4vY7g79vetbhKHsWmCUaU14cE/4jGwJxz7cUX5JURUIArZeU/ycWx9/f3sgrH//2pe8NWDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"966d9803056dfeacc329e1fba70a18e0ba3eb505a73144adc438dd1835bc8a98","last_reissued_at":"2026-05-17T23:59:00.582506Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:00.582506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1812.01802","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"emeou9aCr4JEuUcqb/l8GnJJkqCBtJ2gyKFb1wGm8/4YkANQH8+Wo+shZZ4zlxQUaiC3Uk2ztiYTmeru6znxCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:28:19.340176Z"},"content_sha256":"c99983de5ac591a6085cbbfb3a666ef6313a00743ac2e9df223590803112a902","schema_version":"1.0","event_id":"sha256:c99983de5ac591a6085cbbfb3a666ef6313a00743ac2e9df223590803112a902"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:SZWZQAYFNX7KZQZJ4H52OCQY4C","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Visual Attention for Behavioral Cloning in Autonomous Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Pabitra Mitra, Sourav Pal, Tharun Mohandoss","submitted_at":"2018-12-05T03:30:54Z","abstract_excerpt":"The goal of our work is to use visual attention to enhance autonomous driving performance. We present two methods of predicting visual attention maps. The first method is a supervised learning approach in which we collect eye-gaze data for the task of driving and use this to train a model for predicting the attention map. The second method is a novel unsupervised approach where we train a model to learn to predict attention as it learns to drive a car. Finally, we present a comparative study of our results and show that the supervised approach for predicting attention when incorporated perform"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01802","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gS19BxTaOjS77gp1dcPQVPCQIS6+aEYuWggngW5dWdigY/7oZrPRcs0yUJzxgJmEPQW9l1FmeDeNDNcq1ppaCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:28:19.340549Z"},"content_sha256":"32410f2ba7484a4136bb8cc892e5f3db16931405ece4f14a0cbb223118819b41","schema_version":"1.0","event_id":"sha256:32410f2ba7484a4136bb8cc892e5f3db16931405ece4f14a0cbb223118819b41"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SZWZQAYFNX7KZQZJ4H52OCQY4C/bundle.json","state_url":"https://pith.science/pith/SZWZQAYFNX7KZQZJ4H52OCQY4C/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SZWZQAYFNX7KZQZJ4H52OCQY4C/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":"2026-06-02T12:28:19Z","links":{"resolver":"https://pith.science/pith/SZWZQAYFNX7KZQZJ4H52OCQY4C","bundle":"https://pith.science/pith/SZWZQAYFNX7KZQZJ4H52OCQY4C/bundle.json","state":"https://pith.science/pith/SZWZQAYFNX7KZQZJ4H52OCQY4C/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SZWZQAYFNX7KZQZJ4H52OCQY4C/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:SZWZQAYFNX7KZQZJ4H52OCQY4C","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":"ecec2a769d49e44b93f9b269a3cd903f6388614dc720b99107e2a3950c1d5189","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-12-05T03:30:54Z","title_canon_sha256":"7d10d2d468b5d85e0a14b3b9e3ba0c8521a71dc9232d95c854d4cec315f1ca08"},"schema_version":"1.0","source":{"id":"1812.01802","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1812.01802","created_at":"2026-05-17T23:59:00Z"},{"alias_kind":"arxiv_version","alias_value":"1812.01802v1","created_at":"2026-05-17T23:59:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.01802","created_at":"2026-05-17T23:59:00Z"},{"alias_kind":"pith_short_12","alias_value":"SZWZQAYFNX7K","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"SZWZQAYFNX7KZQZJ","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"SZWZQAYF","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:32410f2ba7484a4136bb8cc892e5f3db16931405ece4f14a0cbb223118819b41","target":"graph","created_at":"2026-05-17T23:59:00Z","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":"The goal of our work is to use visual attention to enhance autonomous driving performance. We present two methods of predicting visual attention maps. The first method is a supervised learning approach in which we collect eye-gaze data for the task of driving and use this to train a model for predicting the attention map. The second method is a novel unsupervised approach where we train a model to learn to predict attention as it learns to drive a car. Finally, we present a comparative study of our results and show that the supervised approach for predicting attention when incorporated perform","authors_text":"Pabitra Mitra, Sourav Pal, Tharun Mohandoss","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-12-05T03:30:54Z","title":"Visual Attention for Behavioral Cloning in Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.01802","kind":"arxiv","version":1},"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:c99983de5ac591a6085cbbfb3a666ef6313a00743ac2e9df223590803112a902","target":"record","created_at":"2026-05-17T23:59:00Z","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":"ecec2a769d49e44b93f9b269a3cd903f6388614dc720b99107e2a3950c1d5189","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-12-05T03:30:54Z","title_canon_sha256":"7d10d2d468b5d85e0a14b3b9e3ba0c8521a71dc9232d95c854d4cec315f1ca08"},"schema_version":"1.0","source":{"id":"1812.01802","kind":"arxiv","version":1}},"canonical_sha256":"966d9803056dfeacc329e1fba70a18e0ba3eb505a73144adc438dd1835bc8a98","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"966d9803056dfeacc329e1fba70a18e0ba3eb505a73144adc438dd1835bc8a98","first_computed_at":"2026-05-17T23:59:00.582506Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:00.582506Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WFnVt0bSK/veFQd4vY7g79vetbhKHsWmCUaU14cE/4jGwJxz7cUX5JURUIArZeU/ycWx9/f3sgrH//2pe8NWDQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:00.582943Z","signed_message":"canonical_sha256_bytes"},"source_id":"1812.01802","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c99983de5ac591a6085cbbfb3a666ef6313a00743ac2e9df223590803112a902","sha256:32410f2ba7484a4136bb8cc892e5f3db16931405ece4f14a0cbb223118819b41"],"state_sha256":"9a2d24c01b12cbc36ae79f2e839d9f17926e46a4ebbe92536a2b30178c2cbe13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cC3vCDtX+Ct8t6GYj0yoc7wjGK6/d8c5ovolzR7yNa4rELkZFPFsm+5Im0oQHpnQh78WHYpQmGGQomTggpS4Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T12:28:19.343129Z","bundle_sha256":"770f885f84a06c58b209df335d1a4e6d448d0f780b7a244dd9c1a0adfde30880"}}