{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LPTTSY7W4RMLGLGL5E2W7DZOQI","short_pith_number":"pith:LPTTSY7W","schema_version":"1.0","canonical_sha256":"5be73963f6e458b32ccbe9356f8f2e82043d13ba669b391b98dfed750a23c03c","source":{"kind":"arxiv","id":"1801.03905","version":1},"attestation_state":"computed","paper":{"title":"Learning and Inferring a Driver's Braking Action in Car-Following Scenarios","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Ding Zhao, Junqiang Xi, Wenshuo Wang","submitted_at":"2018-01-11T18:05:41Z","abstract_excerpt":"Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems and avoiding collisions. In this paper we focus on predicting a driver's intent to brake in car-following scenarios from a perception-decision-action perspective according to his/her driving history. A learning-based inference method, using onboard data from CAN-Bus, radar and cameras as explanatory variables, is introduced to infer drivers' braking decisions by combining a Gaussian mixture model (GMM) with a hidden Markov model (HMM). The GMM is used to model stochastic relationships amo"},"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":"1801.03905","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-01-11T18:05:41Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"48975e88daa04d22035614510629f48bf0a8c3ec9600ed5d73179a09399557ce","abstract_canon_sha256":"0c9e2f2443f92b2eeb5e70a95f74383caeaa8dca1018b07df08b22872643ccf4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:07.072491Z","signature_b64":"fOYxASDAwQhlAY5eLyBY0GC5o3lv5qw5v/Sk8hkUSBjFwiFp3DSotc42lzweOnGl8VJkl2yTxtrySpThMGtGDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5be73963f6e458b32ccbe9356f8f2e82043d13ba669b391b98dfed750a23c03c","last_reissued_at":"2026-05-18T00:26:07.071782Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:07.071782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning and Inferring a Driver's Braking Action in Car-Following Scenarios","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.LG","authors_text":"Ding Zhao, Junqiang Xi, Wenshuo Wang","submitted_at":"2018-01-11T18:05:41Z","abstract_excerpt":"Accurately predicting and inferring a driver's decision to brake is critical for designing warning systems and avoiding collisions. In this paper we focus on predicting a driver's intent to brake in car-following scenarios from a perception-decision-action perspective according to his/her driving history. A learning-based inference method, using onboard data from CAN-Bus, radar and cameras as explanatory variables, is introduced to infer drivers' braking decisions by combining a Gaussian mixture model (GMM) with a hidden Markov model (HMM). The GMM is used to model stochastic relationships amo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.03905","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":"1801.03905","created_at":"2026-05-18T00:26:07.071903+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.03905v1","created_at":"2026-05-18T00:26:07.071903+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.03905","created_at":"2026-05-18T00:26:07.071903+00:00"},{"alias_kind":"pith_short_12","alias_value":"LPTTSY7W4RML","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LPTTSY7W4RMLGLGL","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LPTTSY7W","created_at":"2026-05-18T12:32:37.024351+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/LPTTSY7W4RMLGLGL5E2W7DZOQI","json":"https://pith.science/pith/LPTTSY7W4RMLGLGL5E2W7DZOQI.json","graph_json":"https://pith.science/api/pith-number/LPTTSY7W4RMLGLGL5E2W7DZOQI/graph.json","events_json":"https://pith.science/api/pith-number/LPTTSY7W4RMLGLGL5E2W7DZOQI/events.json","paper":"https://pith.science/paper/LPTTSY7W"},"agent_actions":{"view_html":"https://pith.science/pith/LPTTSY7W4RMLGLGL5E2W7DZOQI","download_json":"https://pith.science/pith/LPTTSY7W4RMLGLGL5E2W7DZOQI.json","view_paper":"https://pith.science/paper/LPTTSY7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.03905&json=true","fetch_graph":"https://pith.science/api/pith-number/LPTTSY7W4RMLGLGL5E2W7DZOQI/graph.json","fetch_events":"https://pith.science/api/pith-number/LPTTSY7W4RMLGLGL5E2W7DZOQI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LPTTSY7W4RMLGLGL5E2W7DZOQI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LPTTSY7W4RMLGLGL5E2W7DZOQI/action/storage_attestation","attest_author":"https://pith.science/pith/LPTTSY7W4RMLGLGL5E2W7DZOQI/action/author_attestation","sign_citation":"https://pith.science/pith/LPTTSY7W4RMLGLGL5E2W7DZOQI/action/citation_signature","submit_replication":"https://pith.science/pith/LPTTSY7W4RMLGLGL5E2W7DZOQI/action/replication_record"}},"created_at":"2026-05-18T00:26:07.071903+00:00","updated_at":"2026-05-18T00:26:07.071903+00:00"}