{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:VDDEJP54APOLHAY2R6TYS7T6VY","short_pith_number":"pith:VDDEJP54","canonical_record":{"source":{"id":"1708.08986","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T03:36:30Z","cross_cats_sorted":[],"title_canon_sha256":"23ae67e7a2635a7fc175612d5db438f9a02a34e609a36ec96028ff0d0b71585e","abstract_canon_sha256":"56b47335d985a329ecbfa033027fc45f368f10ae83b1d3497ce16cfb53db5c24"},"schema_version":"1.0"},"canonical_sha256":"a8c644bfbc03dcb3831a8fa7897e7eae1b3ef59bd2a6dabe132527f12f6e59cd","source":{"kind":"arxiv","id":"1708.08986","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.08986","created_at":"2026-05-18T00:36:21Z"},{"alias_kind":"arxiv_version","alias_value":"1708.08986v1","created_at":"2026-05-18T00:36:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.08986","created_at":"2026-05-18T00:36:21Z"},{"alias_kind":"pith_short_12","alias_value":"VDDEJP54APOL","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VDDEJP54APOLHAY2","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VDDEJP54","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:VDDEJP54APOLHAY2R6TYS7T6VY","target":"record","payload":{"canonical_record":{"source":{"id":"1708.08986","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T03:36:30Z","cross_cats_sorted":[],"title_canon_sha256":"23ae67e7a2635a7fc175612d5db438f9a02a34e609a36ec96028ff0d0b71585e","abstract_canon_sha256":"56b47335d985a329ecbfa033027fc45f368f10ae83b1d3497ce16cfb53db5c24"},"schema_version":"1.0"},"canonical_sha256":"a8c644bfbc03dcb3831a8fa7897e7eae1b3ef59bd2a6dabe132527f12f6e59cd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:36:21.482850Z","signature_b64":"gF+C/QhrSCC7DrFZ0CbBNlXy3vpNxBcR2Zp43ob97+zw6cEsiAifNuUZ7ALwMciMaNSXoy8RL+QMwj8adN19AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8c644bfbc03dcb3831a8fa7897e7eae1b3ef59bd2a6dabe132527f12f6e59cd","last_reissued_at":"2026-05-18T00:36:21.482293Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:36:21.482293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.08986","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":"2026-05-18T00:36:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ep8dlsEvKIWr2VDD1M9gjih9Mnpo5blawiNnlXqx5t6llNVHTXiSr6D4qMyu+F+ZlW7dbobXSiMsxd2UIgubBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T14:15:59.136427Z"},"content_sha256":"2371dbdc48a49955d99510f21529d4a290d783c16042fe1bb28a7b3a58349cb1","schema_version":"1.0","event_id":"sha256:2371dbdc48a49955d99510f21529d4a290d783c16042fe1bb28a7b3a58349cb1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:VDDEJP54APOLHAY2R6TYS7T6VY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ding Zhao, Junqiang Xi, Wenshuo Wang","submitted_at":"2017-08-16T03:36:30Z","abstract_excerpt":"Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns. In the Bayesian nonparametric approach, we utilize a hierarchical Dirichlet process"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.08986","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":"2026-05-18T00:36:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WOiNDwmwYUjjy+ZTE7l2WcF+xIoXyca2LnTx/ZIdmrWoYg3bvYiOZEpZ6c+kWZSFM/8tP1u0cdq3ix0Og1dxCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T14:15:59.137125Z"},"content_sha256":"110c064d93823c9012d5ece043693e5598da3002b66cf4b8109c6148def15437","schema_version":"1.0","event_id":"sha256:110c064d93823c9012d5ece043693e5598da3002b66cf4b8109c6148def15437"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VDDEJP54APOLHAY2R6TYS7T6VY/bundle.json","state_url":"https://pith.science/pith/VDDEJP54APOLHAY2R6TYS7T6VY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VDDEJP54APOLHAY2R6TYS7T6VY/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":"2026-05-22T14:15:59Z","links":{"resolver":"https://pith.science/pith/VDDEJP54APOLHAY2R6TYS7T6VY","bundle":"https://pith.science/pith/VDDEJP54APOLHAY2R6TYS7T6VY/bundle.json","state":"https://pith.science/pith/VDDEJP54APOLHAY2R6TYS7T6VY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VDDEJP54APOLHAY2R6TYS7T6VY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:VDDEJP54APOLHAY2R6TYS7T6VY","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":"56b47335d985a329ecbfa033027fc45f368f10ae83b1d3497ce16cfb53db5c24","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T03:36:30Z","title_canon_sha256":"23ae67e7a2635a7fc175612d5db438f9a02a34e609a36ec96028ff0d0b71585e"},"schema_version":"1.0","source":{"id":"1708.08986","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.08986","created_at":"2026-05-18T00:36:21Z"},{"alias_kind":"arxiv_version","alias_value":"1708.08986v1","created_at":"2026-05-18T00:36:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.08986","created_at":"2026-05-18T00:36:21Z"},{"alias_kind":"pith_short_12","alias_value":"VDDEJP54APOL","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"VDDEJP54APOLHAY2","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"VDDEJP54","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:110c064d93823c9012d5ece043693e5598da3002b66cf4b8109c6148def15437","target":"graph","created_at":"2026-05-18T00:36:21Z","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":"Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns. In the Bayesian nonparametric approach, we utilize a hierarchical Dirichlet process","authors_text":"Ding Zhao, Junqiang Xi, Wenshuo Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T03:36:30Z","title":"Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.08986","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":"sha256:2371dbdc48a49955d99510f21529d4a290d783c16042fe1bb28a7b3a58349cb1","target":"record","created_at":"2026-05-18T00:36:21Z","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":"56b47335d985a329ecbfa033027fc45f368f10ae83b1d3497ce16cfb53db5c24","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-16T03:36:30Z","title_canon_sha256":"23ae67e7a2635a7fc175612d5db438f9a02a34e609a36ec96028ff0d0b71585e"},"schema_version":"1.0","source":{"id":"1708.08986","kind":"arxiv","version":1}},"canonical_sha256":"a8c644bfbc03dcb3831a8fa7897e7eae1b3ef59bd2a6dabe132527f12f6e59cd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a8c644bfbc03dcb3831a8fa7897e7eae1b3ef59bd2a6dabe132527f12f6e59cd","first_computed_at":"2026-05-18T00:36:21.482293Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:36:21.482293Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gF+C/QhrSCC7DrFZ0CbBNlXy3vpNxBcR2Zp43ob97+zw6cEsiAifNuUZ7ALwMciMaNSXoy8RL+QMwj8adN19AA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:36:21.482850Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.08986","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2371dbdc48a49955d99510f21529d4a290d783c16042fe1bb28a7b3a58349cb1","sha256:110c064d93823c9012d5ece043693e5598da3002b66cf4b8109c6148def15437"],"state_sha256":"f400c083f5032b81e1673010f3aac138464bda5dce2cda6ee40b8b3886e4b34d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T5ndyRCGKGXnS2J95KPekzfpi8hyoeORRLE1uadOR8D4uRgaFOVTBAmxxHmogAFFC48vQmi2P3ZeRfzCr6CkBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T14:15:59.139240Z","bundle_sha256":"f4d9437b0db3bfa842578c4d198168e447d48a506085e5c343eb5bb601fb3004"}}