{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:XC3CMXJRHQSZZCUHS67BYQ3YDH","short_pith_number":"pith:XC3CMXJR","schema_version":"1.0","canonical_sha256":"b8b6265d313c259c8a8797be1c437819fe5ad0af0d9aa5b0c7d86c9cc077d49c","source":{"kind":"arxiv","id":"1902.09068","version":1},"attestation_state":"computed","paper":{"title":"A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kan Zheng, Long Zhao, Pingzhi Fan, Shiwen Liu","submitted_at":"2019-02-25T02:46:44Z","abstract_excerpt":"In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on Hidden Markov Model (HMM) is proposed for autonomous vehicles. HMMs representing different driving intentions are trained and tested with field collected data from a flyover. When training the models, either discrete or continuous characterization of the mobility features of vehicles is applied."},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1902.09068","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-25T02:46:44Z","cross_cats_sorted":["cs.RO","stat.ML"],"title_canon_sha256":"ea0d590195a811f10bae63113d7db174a090360c21b2f2f280faa7b23e1a221b","abstract_canon_sha256":"42cb8cc813cf1652dd690238607cc5bbe593f87636320c7bf904fde192a1c2a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:46.756740Z","signature_b64":"pUgfsWu7TaOnEIsMO6OryIWNg6Rq3j94i2I7+g+F96DmamR+WQTLzuqU5NIDllrAXJsROnvsike1Ti5+9AQCCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8b6265d313c259c8a8797be1c437819fe5ad0af0d9aa5b0c7d86c9cc077d49c","last_reissued_at":"2026-05-17T23:52:46.756080Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:46.756080Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Driving Intention Prediction Method Based on Hidden Markov Model for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO","stat.ML"],"primary_cat":"cs.LG","authors_text":"Kan Zheng, Long Zhao, Pingzhi Fan, Shiwen Liu","submitted_at":"2019-02-25T02:46:44Z","abstract_excerpt":"In a mixed-traffic scenario where both autonomous vehicles and human-driving vehicles exist, a timely prediction of driving intentions of nearby human-driving vehicles is essential for the safe and efficient driving of an autonomous vehicle. In this paper, a driving intention prediction method based on Hidden Markov Model (HMM) is proposed for autonomous vehicles. HMMs representing different driving intentions are trained and tested with field collected data from a flyover. When training the models, either discrete or continuous characterization of the mobility features of vehicles is applied."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09068","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1902.09068","created_at":"2026-05-17T23:52:46.756170+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.09068v1","created_at":"2026-05-17T23:52:46.756170+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09068","created_at":"2026-05-17T23:52:46.756170+00:00"},{"alias_kind":"pith_short_12","alias_value":"XC3CMXJRHQSZ","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"XC3CMXJRHQSZZCUH","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"XC3CMXJR","created_at":"2026-05-18T12:33:33.725879+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XC3CMXJRHQSZZCUHS67BYQ3YDH","json":"https://pith.science/pith/XC3CMXJRHQSZZCUHS67BYQ3YDH.json","graph_json":"https://pith.science/api/pith-number/XC3CMXJRHQSZZCUHS67BYQ3YDH/graph.json","events_json":"https://pith.science/api/pith-number/XC3CMXJRHQSZZCUHS67BYQ3YDH/events.json","paper":"https://pith.science/paper/XC3CMXJR"},"agent_actions":{"view_html":"https://pith.science/pith/XC3CMXJRHQSZZCUHS67BYQ3YDH","download_json":"https://pith.science/pith/XC3CMXJRHQSZZCUHS67BYQ3YDH.json","view_paper":"https://pith.science/paper/XC3CMXJR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.09068&json=true","fetch_graph":"https://pith.science/api/pith-number/XC3CMXJRHQSZZCUHS67BYQ3YDH/graph.json","fetch_events":"https://pith.science/api/pith-number/XC3CMXJRHQSZZCUHS67BYQ3YDH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XC3CMXJRHQSZZCUHS67BYQ3YDH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XC3CMXJRHQSZZCUHS67BYQ3YDH/action/storage_attestation","attest_author":"https://pith.science/pith/XC3CMXJRHQSZZCUHS67BYQ3YDH/action/author_attestation","sign_citation":"https://pith.science/pith/XC3CMXJRHQSZZCUHS67BYQ3YDH/action/citation_signature","submit_replication":"https://pith.science/pith/XC3CMXJRHQSZZCUHS67BYQ3YDH/action/replication_record"}},"created_at":"2026-05-17T23:52:46.756170+00:00","updated_at":"2026-05-17T23:52:46.756170+00:00"}