{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:KSSDYHCM2TQYLPCUBG2M5WPXWB","short_pith_number":"pith:KSSDYHCM","schema_version":"1.0","canonical_sha256":"54a43c1c4cd4e185bc5409b4ced9f7b07d32bf60a8f01bcc3d5791e28c668748","source":{"kind":"arxiv","id":"1707.02515","version":1},"attestation_state":"computed","paper":{"title":"A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"cs.AI","authors_text":"Cheng Peng, Liting Sun, Masayoshi Tomizuka, Wei Zhan","submitted_at":"2017-07-09T02:00:21Z","abstract_excerpt":"For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined"},"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":"1707.02515","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2017-07-09T02:00:21Z","cross_cats_sorted":["cs.LG","cs.SY"],"title_canon_sha256":"074b328aadeb0c03f2c3656625ca90c61f5cf87a8801f077867b7a9e9110c0f6","abstract_canon_sha256":"6eaf28e41294b2a48320f19e2166bacbae62532714ef101a7bd1002bbed2ca8d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:40:36.627153Z","signature_b64":"1voWan8Zgx6wx9GP2Xn7SHRReiB83Ps6lakJyMLBiOQCa8Ubq5j0A6eIDvBT3hnsEWUw9NfOBo2HmpuGDUgtAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54a43c1c4cd4e185bc5409b4ced9f7b07d32bf60a8f01bcc3d5791e28c668748","last_reissued_at":"2026-05-18T00:40:36.626502Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:40:36.626502Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"cs.AI","authors_text":"Cheng Peng, Liting Sun, Masayoshi Tomizuka, Wei Zhan","submitted_at":"2017-07-09T02:00:21Z","abstract_excerpt":"For safe and efficient planning and control in autonomous driving, we need a driving policy which can achieve desirable driving quality in long-term horizon with guaranteed safety and feasibility. Optimization-based approaches, such as Model Predictive Control (MPC), can provide such optimal policies, but their computational complexity is generally unacceptable for real-time implementation. To address this problem, we propose a fast integrated planning and control framework that combines learning- and optimization-based approaches in a two-layer hierarchical structure. The first layer, defined"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.02515","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":"1707.02515","created_at":"2026-05-18T00:40:36.626623+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.02515v1","created_at":"2026-05-18T00:40:36.626623+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.02515","created_at":"2026-05-18T00:40:36.626623+00:00"},{"alias_kind":"pith_short_12","alias_value":"KSSDYHCM2TQY","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"KSSDYHCM2TQYLPCU","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"KSSDYHCM","created_at":"2026-05-18T12:31:28.150371+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/KSSDYHCM2TQYLPCUBG2M5WPXWB","json":"https://pith.science/pith/KSSDYHCM2TQYLPCUBG2M5WPXWB.json","graph_json":"https://pith.science/api/pith-number/KSSDYHCM2TQYLPCUBG2M5WPXWB/graph.json","events_json":"https://pith.science/api/pith-number/KSSDYHCM2TQYLPCUBG2M5WPXWB/events.json","paper":"https://pith.science/paper/KSSDYHCM"},"agent_actions":{"view_html":"https://pith.science/pith/KSSDYHCM2TQYLPCUBG2M5WPXWB","download_json":"https://pith.science/pith/KSSDYHCM2TQYLPCUBG2M5WPXWB.json","view_paper":"https://pith.science/paper/KSSDYHCM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.02515&json=true","fetch_graph":"https://pith.science/api/pith-number/KSSDYHCM2TQYLPCUBG2M5WPXWB/graph.json","fetch_events":"https://pith.science/api/pith-number/KSSDYHCM2TQYLPCUBG2M5WPXWB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KSSDYHCM2TQYLPCUBG2M5WPXWB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KSSDYHCM2TQYLPCUBG2M5WPXWB/action/storage_attestation","attest_author":"https://pith.science/pith/KSSDYHCM2TQYLPCUBG2M5WPXWB/action/author_attestation","sign_citation":"https://pith.science/pith/KSSDYHCM2TQYLPCUBG2M5WPXWB/action/citation_signature","submit_replication":"https://pith.science/pith/KSSDYHCM2TQYLPCUBG2M5WPXWB/action/replication_record"}},"created_at":"2026-05-18T00:40:36.626623+00:00","updated_at":"2026-05-18T00:40:36.626623+00:00"}