{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:JLF23KEEMJNDFI6KNLCAZAUIOG","short_pith_number":"pith:JLF23KEE","schema_version":"1.0","canonical_sha256":"4acbada884625a32a3ca6ac40c828871af52f4ce482df0499e1b412fe1d2d301","source":{"kind":"arxiv","id":"2310.02843","version":1},"attestation_state":"computed","paper":{"title":"Incorporating Target Vehicle Trajectories Predicted by Deep Learning Into Model Predictive Controlled Vehicles","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jizheng Liu, Marion Leibold, Martin Buss, Ni Dang, Zengjie Zhang","submitted_at":"2023-10-04T14:20:50Z","abstract_excerpt":"Model Predictive Control (MPC) has been widely applied to the motion planning of autonomous vehicles. An MPC-controlled vehicle is required to predict its own trajectories in a finite prediction horizon according to its model. Beyond this, the vehicle should also incorporate the prediction of the trajectory of its nearby vehicles, or target vehicles (TVs) into its decision-making. The conventional trajectory prediction methods, such as the constant-speed-based ones, are too trivial to accurately capture the potential collision risks. In this report, we propose a novel MPC-based motion planning"},"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":"2310.02843","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.RO","submitted_at":"2023-10-04T14:20:50Z","cross_cats_sorted":[],"title_canon_sha256":"8312369c2672fc42a8b5e7c646a7ccd87adeb4a2eafd59fdff179fcf0cec78eb","abstract_canon_sha256":"186725e8f55dcf5aef06d91e52d00166a25022a47eb5fd6cd4deba97a44cd55d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:57:14.557897Z","signature_b64":"c1t/MB0XnDXqDt2kLDzpLKBhSEtZ4Wnuw0VqZDjQmcUh8TEkTFjoT7kZSB33wenj/eHHlhQ27knl2I17PWg9Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4acbada884625a32a3ca6ac40c828871af52f4ce482df0499e1b412fe1d2d301","last_reissued_at":"2026-07-05T06:57:14.557338Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:57:14.557338Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Incorporating Target Vehicle Trajectories Predicted by Deep Learning Into Model Predictive Controlled Vehicles","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Jizheng Liu, Marion Leibold, Martin Buss, Ni Dang, Zengjie Zhang","submitted_at":"2023-10-04T14:20:50Z","abstract_excerpt":"Model Predictive Control (MPC) has been widely applied to the motion planning of autonomous vehicles. An MPC-controlled vehicle is required to predict its own trajectories in a finite prediction horizon according to its model. Beyond this, the vehicle should also incorporate the prediction of the trajectory of its nearby vehicles, or target vehicles (TVs) into its decision-making. The conventional trajectory prediction methods, such as the constant-speed-based ones, are too trivial to accurately capture the potential collision risks. In this report, we propose a novel MPC-based motion planning"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02843","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.02843/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2310.02843","created_at":"2026-07-05T06:57:14.557395+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.02843v1","created_at":"2026-07-05T06:57:14.557395+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02843","created_at":"2026-07-05T06:57:14.557395+00:00"},{"alias_kind":"pith_short_12","alias_value":"JLF23KEEMJND","created_at":"2026-07-05T06:57:14.557395+00:00"},{"alias_kind":"pith_short_16","alias_value":"JLF23KEEMJNDFI6K","created_at":"2026-07-05T06:57:14.557395+00:00"},{"alias_kind":"pith_short_8","alias_value":"JLF23KEE","created_at":"2026-07-05T06:57:14.557395+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.02313","citing_title":"A Vehicle-in-the-Loop Simulator with AI-Powered Digital Twins for Testing Automated Driving Controllers","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JLF23KEEMJNDFI6KNLCAZAUIOG","json":"https://pith.science/pith/JLF23KEEMJNDFI6KNLCAZAUIOG.json","graph_json":"https://pith.science/api/pith-number/JLF23KEEMJNDFI6KNLCAZAUIOG/graph.json","events_json":"https://pith.science/api/pith-number/JLF23KEEMJNDFI6KNLCAZAUIOG/events.json","paper":"https://pith.science/paper/JLF23KEE"},"agent_actions":{"view_html":"https://pith.science/pith/JLF23KEEMJNDFI6KNLCAZAUIOG","download_json":"https://pith.science/pith/JLF23KEEMJNDFI6KNLCAZAUIOG.json","view_paper":"https://pith.science/paper/JLF23KEE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.02843&json=true","fetch_graph":"https://pith.science/api/pith-number/JLF23KEEMJNDFI6KNLCAZAUIOG/graph.json","fetch_events":"https://pith.science/api/pith-number/JLF23KEEMJNDFI6KNLCAZAUIOG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JLF23KEEMJNDFI6KNLCAZAUIOG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JLF23KEEMJNDFI6KNLCAZAUIOG/action/storage_attestation","attest_author":"https://pith.science/pith/JLF23KEEMJNDFI6KNLCAZAUIOG/action/author_attestation","sign_citation":"https://pith.science/pith/JLF23KEEMJNDFI6KNLCAZAUIOG/action/citation_signature","submit_replication":"https://pith.science/pith/JLF23KEEMJNDFI6KNLCAZAUIOG/action/replication_record"}},"created_at":"2026-07-05T06:57:14.557395+00:00","updated_at":"2026-07-05T06:57:14.557395+00:00"}