{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ELEP2JYZG3WQ45DOWL6YUHAQHC","short_pith_number":"pith:ELEP2JYZ","schema_version":"1.0","canonical_sha256":"22c8fd271936ed0e746eb2fd8a1c10388a72773d057f4052062538a0ba6756f6","source":{"kind":"arxiv","id":"2403.07232","version":1},"attestation_state":"computed","paper":{"title":"Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Adam Villaflor, Brian Yang, Huangyuan Su, Jeff Schneider, John Dolan, Katerina Fragkiadaki","submitted_at":"2024-03-12T01:00:52Z","abstract_excerpt":"Significant progress has been made in training multimodal trajectory forecasting models for autonomous driving. However, effectively integrating these models with downstream planners and model-based control approaches is still an open problem. Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to parameterize autoregressive closed-loop models without retraining. We consider recent trajectory prediction approaches which leverage learned anchor embeddings to predict multiple trajectories, finding that these anchor embeddings can param"},"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":"2403.07232","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-03-12T01:00:52Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b6417f60e4ba7c2de3405ba01eb2e71e610484c21f087711acfae7b6ccbe3287","abstract_canon_sha256":"40c47ef9e92f445212e0d3a86ee1a683318afbf9094740822fdfb08582c5dce8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:55:01.018171Z","signature_b64":"jBu5v3JXfzbOuxKZrkqY5YixfCriXKvNOY+/4MHTabELtNrq5xPsI0wp0KQup7/LQmKifALs38CkK7VkCfteCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"22c8fd271936ed0e746eb2fd8a1c10388a72773d057f4052062538a0ba6756f6","last_reissued_at":"2026-07-05T07:55:01.017756Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:55:01.017756Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Adam Villaflor, Brian Yang, Huangyuan Su, Jeff Schneider, John Dolan, Katerina Fragkiadaki","submitted_at":"2024-03-12T01:00:52Z","abstract_excerpt":"Significant progress has been made in training multimodal trajectory forecasting models for autonomous driving. However, effectively integrating these models with downstream planners and model-based control approaches is still an open problem. Although these models have conventionally been evaluated for open-loop prediction, we show that they can be used to parameterize autoregressive closed-loop models without retraining. We consider recent trajectory prediction approaches which leverage learned anchor embeddings to predict multiple trajectories, finding that these anchor embeddings can param"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.07232","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/2403.07232/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":"2403.07232","created_at":"2026-07-05T07:55:01.017813+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.07232v1","created_at":"2026-07-05T07:55:01.017813+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.07232","created_at":"2026-07-05T07:55:01.017813+00:00"},{"alias_kind":"pith_short_12","alias_value":"ELEP2JYZG3WQ","created_at":"2026-07-05T07:55:01.017813+00:00"},{"alias_kind":"pith_short_16","alias_value":"ELEP2JYZG3WQ45DO","created_at":"2026-07-05T07:55:01.017813+00:00"},{"alias_kind":"pith_short_8","alias_value":"ELEP2JYZ","created_at":"2026-07-05T07:55:01.017813+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/ELEP2JYZG3WQ45DOWL6YUHAQHC","json":"https://pith.science/pith/ELEP2JYZG3WQ45DOWL6YUHAQHC.json","graph_json":"https://pith.science/api/pith-number/ELEP2JYZG3WQ45DOWL6YUHAQHC/graph.json","events_json":"https://pith.science/api/pith-number/ELEP2JYZG3WQ45DOWL6YUHAQHC/events.json","paper":"https://pith.science/paper/ELEP2JYZ"},"agent_actions":{"view_html":"https://pith.science/pith/ELEP2JYZG3WQ45DOWL6YUHAQHC","download_json":"https://pith.science/pith/ELEP2JYZG3WQ45DOWL6YUHAQHC.json","view_paper":"https://pith.science/paper/ELEP2JYZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.07232&json=true","fetch_graph":"https://pith.science/api/pith-number/ELEP2JYZG3WQ45DOWL6YUHAQHC/graph.json","fetch_events":"https://pith.science/api/pith-number/ELEP2JYZG3WQ45DOWL6YUHAQHC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ELEP2JYZG3WQ45DOWL6YUHAQHC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ELEP2JYZG3WQ45DOWL6YUHAQHC/action/storage_attestation","attest_author":"https://pith.science/pith/ELEP2JYZG3WQ45DOWL6YUHAQHC/action/author_attestation","sign_citation":"https://pith.science/pith/ELEP2JYZG3WQ45DOWL6YUHAQHC/action/citation_signature","submit_replication":"https://pith.science/pith/ELEP2JYZG3WQ45DOWL6YUHAQHC/action/replication_record"}},"created_at":"2026-07-05T07:55:01.017813+00:00","updated_at":"2026-07-05T07:55:01.017813+00:00"}