{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PYNUBGPREPWPDWDWNONINC5DSU","short_pith_number":"pith:PYNUBGPR","schema_version":"1.0","canonical_sha256":"7e1b4099f123ecf1d8766b9a868ba3952ffe672a46fb6106a7d232f2a3f4f749","source":{"kind":"arxiv","id":"1807.02187","version":2},"attestation_state":"computed","paper":{"title":"Encoding Motion Primitives for Autonomous Vehicles using Virtual Velocity Constraints and Neural Network Scheduling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Mogens Graf Plessen","submitted_at":"2018-07-05T21:44:39Z","abstract_excerpt":"Within the context of trajectory planning for autonomous vehicles this paper proposes methods for efficient encoding of motion primitives in neural networks on top of model-based and gradient-free reinforcement learning. It is distinguished between 5 core aspects: system model, network architecture, training algorithm, training tasks selection and hardware/software implementation. For the system model, a kinematic (3-states-2-controls) and a dynamic (16-states-2-controls) vehicle model are compared. For the network architecture, 3 feedforward structures are compared including weighted skip con"},"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":"1807.02187","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2018-07-05T21:44:39Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ee04767954fa9c3632cf193740956934d30d2b93b9107924a82d07a46c4fd87f","abstract_canon_sha256":"9c0b0314eb8e632a05528acdf82ecffc332a03a005c6f3e07388f886fefc71e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:04:12.752155Z","signature_b64":"uy+wKfiqeRSQFC+iHcmcJuLsLPmpUZ89EEQzJaksqMCpO317U3MsTbR/S52YqasNE5sx1Iwwl2RiorQFK81GAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e1b4099f123ecf1d8766b9a868ba3952ffe672a46fb6106a7d232f2a3f4f749","last_reissued_at":"2026-05-18T00:04:12.751383Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:04:12.751383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Encoding Motion Primitives for Autonomous Vehicles using Virtual Velocity Constraints and Neural Network Scheduling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Mogens Graf Plessen","submitted_at":"2018-07-05T21:44:39Z","abstract_excerpt":"Within the context of trajectory planning for autonomous vehicles this paper proposes methods for efficient encoding of motion primitives in neural networks on top of model-based and gradient-free reinforcement learning. It is distinguished between 5 core aspects: system model, network architecture, training algorithm, training tasks selection and hardware/software implementation. For the system model, a kinematic (3-states-2-controls) and a dynamic (16-states-2-controls) vehicle model are compared. For the network architecture, 3 feedforward structures are compared including weighted skip con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.02187","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":"1807.02187","created_at":"2026-05-18T00:04:12.751510+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.02187v2","created_at":"2026-05-18T00:04:12.751510+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.02187","created_at":"2026-05-18T00:04:12.751510+00:00"},{"alias_kind":"pith_short_12","alias_value":"PYNUBGPREPWP","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"PYNUBGPREPWPDWDW","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"PYNUBGPR","created_at":"2026-05-18T12:32:46.962924+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/PYNUBGPREPWPDWDWNONINC5DSU","json":"https://pith.science/pith/PYNUBGPREPWPDWDWNONINC5DSU.json","graph_json":"https://pith.science/api/pith-number/PYNUBGPREPWPDWDWNONINC5DSU/graph.json","events_json":"https://pith.science/api/pith-number/PYNUBGPREPWPDWDWNONINC5DSU/events.json","paper":"https://pith.science/paper/PYNUBGPR"},"agent_actions":{"view_html":"https://pith.science/pith/PYNUBGPREPWPDWDWNONINC5DSU","download_json":"https://pith.science/pith/PYNUBGPREPWPDWDWNONINC5DSU.json","view_paper":"https://pith.science/paper/PYNUBGPR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.02187&json=true","fetch_graph":"https://pith.science/api/pith-number/PYNUBGPREPWPDWDWNONINC5DSU/graph.json","fetch_events":"https://pith.science/api/pith-number/PYNUBGPREPWPDWDWNONINC5DSU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PYNUBGPREPWPDWDWNONINC5DSU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PYNUBGPREPWPDWDWNONINC5DSU/action/storage_attestation","attest_author":"https://pith.science/pith/PYNUBGPREPWPDWDWNONINC5DSU/action/author_attestation","sign_citation":"https://pith.science/pith/PYNUBGPREPWPDWDWNONINC5DSU/action/citation_signature","submit_replication":"https://pith.science/pith/PYNUBGPREPWPDWDWNONINC5DSU/action/replication_record"}},"created_at":"2026-05-18T00:04:12.751510+00:00","updated_at":"2026-05-18T00:04:12.751510+00:00"}