{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:2KM2SKB3SIQFVNHHCBI35WNQPC","short_pith_number":"pith:2KM2SKB3","schema_version":"1.0","canonical_sha256":"d299a9283b92205ab4e71051bed9b07890c553272ae76cbec5fb4858ca064abe","source":{"kind":"arxiv","id":"1704.06325","version":2},"attestation_state":"computed","paper":{"title":"Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Alberto Bemporad, Bo Wahlberg, Jonas Martensson, Mogens Graf Plessen, Pedro F. Lima","submitted_at":"2017-04-20T20:24:17Z","abstract_excerpt":"This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, obstacle avoidance, and vehicle dimension constraints. Starting from a nonlinear kinematic bicycle model, vehicle dynamics are transformed to a road-aligned coordinate frame with path along the road centerline replacing time as the dependent variable. Space-varying vehicle dimension constraints are linearized around a reference path to pose convex optimization problems. Such constraints do not require to inflate obstacles by safety-margins and therefore maximize performance in very constrained"},"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":"1704.06325","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SY","submitted_at":"2017-04-20T20:24:17Z","cross_cats_sorted":[],"title_canon_sha256":"ad17cd880d81f9b7cf6682d54269a239854a19a92e908b24858ab2480155b4f1","abstract_canon_sha256":"5cfef93fd070ed766c0141fab8b8d154910dca3e14405f584b9099269de58a39"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:52.413477Z","signature_b64":"gmpqcDhyGOQFjF4T6wlMvKSr079MJHWdCg4NAF91/ULuYR40FB9lUMcQv4z9LvSYXRdfAzLAGI7hnGDZkHjrDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d299a9283b92205ab4e71051bed9b07890c553272ae76cbec5fb4858ca064abe","last_reissued_at":"2026-05-18T00:39:52.413008Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:52.413008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trajectory Planning Under Vehicle Dimension Constraints Using Sequential Linear Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.SY","authors_text":"Alberto Bemporad, Bo Wahlberg, Jonas Martensson, Mogens Graf Plessen, Pedro F. Lima","submitted_at":"2017-04-20T20:24:17Z","abstract_excerpt":"This paper presents a spatial-based trajectory planning method for automated vehicles under actuator, obstacle avoidance, and vehicle dimension constraints. Starting from a nonlinear kinematic bicycle model, vehicle dynamics are transformed to a road-aligned coordinate frame with path along the road centerline replacing time as the dependent variable. Space-varying vehicle dimension constraints are linearized around a reference path to pose convex optimization problems. Such constraints do not require to inflate obstacles by safety-margins and therefore maximize performance in very constrained"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.06325","kind":"arxiv","version":2},"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":"1704.06325","created_at":"2026-05-18T00:39:52.413073+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.06325v2","created_at":"2026-05-18T00:39:52.413073+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.06325","created_at":"2026-05-18T00:39:52.413073+00:00"},{"alias_kind":"pith_short_12","alias_value":"2KM2SKB3SIQF","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_16","alias_value":"2KM2SKB3SIQFVNHH","created_at":"2026-05-18T12:30:55.937587+00:00"},{"alias_kind":"pith_short_8","alias_value":"2KM2SKB3","created_at":"2026-05-18T12:30:55.937587+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/2KM2SKB3SIQFVNHHCBI35WNQPC","json":"https://pith.science/pith/2KM2SKB3SIQFVNHHCBI35WNQPC.json","graph_json":"https://pith.science/api/pith-number/2KM2SKB3SIQFVNHHCBI35WNQPC/graph.json","events_json":"https://pith.science/api/pith-number/2KM2SKB3SIQFVNHHCBI35WNQPC/events.json","paper":"https://pith.science/paper/2KM2SKB3"},"agent_actions":{"view_html":"https://pith.science/pith/2KM2SKB3SIQFVNHHCBI35WNQPC","download_json":"https://pith.science/pith/2KM2SKB3SIQFVNHHCBI35WNQPC.json","view_paper":"https://pith.science/paper/2KM2SKB3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.06325&json=true","fetch_graph":"https://pith.science/api/pith-number/2KM2SKB3SIQFVNHHCBI35WNQPC/graph.json","fetch_events":"https://pith.science/api/pith-number/2KM2SKB3SIQFVNHHCBI35WNQPC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2KM2SKB3SIQFVNHHCBI35WNQPC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2KM2SKB3SIQFVNHHCBI35WNQPC/action/storage_attestation","attest_author":"https://pith.science/pith/2KM2SKB3SIQFVNHHCBI35WNQPC/action/author_attestation","sign_citation":"https://pith.science/pith/2KM2SKB3SIQFVNHHCBI35WNQPC/action/citation_signature","submit_replication":"https://pith.science/pith/2KM2SKB3SIQFVNHHCBI35WNQPC/action/replication_record"}},"created_at":"2026-05-18T00:39:52.413073+00:00","updated_at":"2026-05-18T00:39:52.413073+00:00"}