{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:GJDRLGIRV2BLYHQCMGHF33DJLC","short_pith_number":"pith:GJDRLGIR","canonical_record":{"source":{"id":"1504.01716","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2015-04-07T19:41:59Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e2788123f9443e3a26e70c7f317a5b0a53874d36c1e6b3533e904a23d245bcbd","abstract_canon_sha256":"4a7c3155952b70e068d03113a3e7cc9e0fb6f46c0fa8e9b545b7803f6d4abc3e"},"schema_version":"1.0"},"canonical_sha256":"3247159911ae82bc1e02618e5dec69589a007795db983ea65a3692ceb33ff4b5","source":{"kind":"arxiv","id":"1504.01716","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.01716","created_at":"2026-05-18T02:18:32Z"},{"alias_kind":"arxiv_version","alias_value":"1504.01716v3","created_at":"2026-05-18T02:18:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.01716","created_at":"2026-05-18T02:18:32Z"},{"alias_kind":"pith_short_12","alias_value":"GJDRLGIRV2BL","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"GJDRLGIRV2BLYHQC","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"GJDRLGIR","created_at":"2026-05-18T12:29:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:GJDRLGIRV2BLYHQCMGHF33DJLC","target":"record","payload":{"canonical_record":{"source":{"id":"1504.01716","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2015-04-07T19:41:59Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e2788123f9443e3a26e70c7f317a5b0a53874d36c1e6b3533e904a23d245bcbd","abstract_canon_sha256":"4a7c3155952b70e068d03113a3e7cc9e0fb6f46c0fa8e9b545b7803f6d4abc3e"},"schema_version":"1.0"},"canonical_sha256":"3247159911ae82bc1e02618e5dec69589a007795db983ea65a3692ceb33ff4b5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:18:32.610228Z","signature_b64":"17oFd2M+ZEQo2Ha+uJMffGIbAr1heU0M6kNfXbWF4GbUh6XrIyrP9yC3w6Bg+IBBryY1HBroY0zTVTSs4gFUAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3247159911ae82bc1e02618e5dec69589a007795db983ea65a3692ceb33ff4b5","last_reissued_at":"2026-05-18T02:18:32.608665Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:18:32.608665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1504.01716","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":"2026-05-18T02:18:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nR/6bPg2BtYgQcswj4rqKyoFCUlcLy18FY06HhPnxlA+Xev6tZcs96moTuA/dGHJSmCf9b0IzFx2ZnPT7ZwsBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T20:01:06.239505Z"},"content_sha256":"c9fb84f5e2c3bb5ae2327457820b1d2f36f6abdd9c9320238ade16e59b59e937","schema_version":"1.0","event_id":"sha256:c9fb84f5e2c3bb5ae2327457820b1d2f36f6abdd9c9320238ade16e59b59e937"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:GJDRLGIRV2BLYHQCMGHF33DJLC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Empirical Evaluation of Deep Learning on Highway Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Adam Coates, Andrew Y. Ng, Brody Huval, Fernando Mujica, Jeff Kiske, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Royce Cheng-Yue, Sameep Tandon, Tao Wang, Toki Migimatsu, Will Song","submitted_at":"2015-04-07T19:41:59Z","abstract_excerpt":"Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and ap"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.01716","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":"2026-05-18T02:18:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c8mOtl1YKr8l5v0I4cWseedC7EEcaywiaAkmSsNUNH4XtZbbHRzNjcn0HZdrTtAw+A48DjvtjTglrIfxAKxoAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T20:01:06.240304Z"},"content_sha256":"f782140a7f279a97dde2b2409553a599f0f159c0fdaae16f6d812d90054553a2","schema_version":"1.0","event_id":"sha256:f782140a7f279a97dde2b2409553a599f0f159c0fdaae16f6d812d90054553a2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GJDRLGIRV2BLYHQCMGHF33DJLC/bundle.json","state_url":"https://pith.science/pith/GJDRLGIRV2BLYHQCMGHF33DJLC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GJDRLGIRV2BLYHQCMGHF33DJLC/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":"2026-05-26T20:01:06Z","links":{"resolver":"https://pith.science/pith/GJDRLGIRV2BLYHQCMGHF33DJLC","bundle":"https://pith.science/pith/GJDRLGIRV2BLYHQCMGHF33DJLC/bundle.json","state":"https://pith.science/pith/GJDRLGIRV2BLYHQCMGHF33DJLC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GJDRLGIRV2BLYHQCMGHF33DJLC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:GJDRLGIRV2BLYHQCMGHF33DJLC","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":"4a7c3155952b70e068d03113a3e7cc9e0fb6f46c0fa8e9b545b7803f6d4abc3e","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2015-04-07T19:41:59Z","title_canon_sha256":"e2788123f9443e3a26e70c7f317a5b0a53874d36c1e6b3533e904a23d245bcbd"},"schema_version":"1.0","source":{"id":"1504.01716","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1504.01716","created_at":"2026-05-18T02:18:32Z"},{"alias_kind":"arxiv_version","alias_value":"1504.01716v3","created_at":"2026-05-18T02:18:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1504.01716","created_at":"2026-05-18T02:18:32Z"},{"alias_kind":"pith_short_12","alias_value":"GJDRLGIRV2BL","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"GJDRLGIRV2BLYHQC","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"GJDRLGIR","created_at":"2026-05-18T12:29:22Z"}],"graph_snapshots":[{"event_id":"sha256:f782140a7f279a97dde2b2409553a599f0f159c0fdaae16f6d812d90054553a2","target":"graph","created_at":"2026-05-18T02:18:32Z","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":"Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and ap","authors_text":"Adam Coates, Andrew Y. Ng, Brody Huval, Fernando Mujica, Jeff Kiske, Joel Pazhayampallil, Mykhaylo Andriluka, Pranav Rajpurkar, Royce Cheng-Yue, Sameep Tandon, Tao Wang, Toki Migimatsu, Will Song","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2015-04-07T19:41:59Z","title":"An Empirical Evaluation of Deep Learning on Highway Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1504.01716","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:c9fb84f5e2c3bb5ae2327457820b1d2f36f6abdd9c9320238ade16e59b59e937","target":"record","created_at":"2026-05-18T02:18:32Z","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":"4a7c3155952b70e068d03113a3e7cc9e0fb6f46c0fa8e9b545b7803f6d4abc3e","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2015-04-07T19:41:59Z","title_canon_sha256":"e2788123f9443e3a26e70c7f317a5b0a53874d36c1e6b3533e904a23d245bcbd"},"schema_version":"1.0","source":{"id":"1504.01716","kind":"arxiv","version":3}},"canonical_sha256":"3247159911ae82bc1e02618e5dec69589a007795db983ea65a3692ceb33ff4b5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3247159911ae82bc1e02618e5dec69589a007795db983ea65a3692ceb33ff4b5","first_computed_at":"2026-05-18T02:18:32.608665Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:18:32.608665Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"17oFd2M+ZEQo2Ha+uJMffGIbAr1heU0M6kNfXbWF4GbUh6XrIyrP9yC3w6Bg+IBBryY1HBroY0zTVTSs4gFUAw==","signature_status":"signed_v1","signed_at":"2026-05-18T02:18:32.610228Z","signed_message":"canonical_sha256_bytes"},"source_id":"1504.01716","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c9fb84f5e2c3bb5ae2327457820b1d2f36f6abdd9c9320238ade16e59b59e937","sha256:f782140a7f279a97dde2b2409553a599f0f159c0fdaae16f6d812d90054553a2"],"state_sha256":"d9dc5b897064d02fd8999934179a808d9f2e1019726d2d40f0b182e0eee73d6f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GQeIvkUY0O09yD6Gsytk/fR7rth4Q/unLNNO9ADX9hs0aXsuRcYlTOAhduR9vBGnPrR7KTcZSMpJzP926UHLCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T20:01:06.244225Z","bundle_sha256":"ec6e2a0487720b7b47714a02bd2460e6621ceef100c26c423526a5689a46487c"}}