{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:Q7QO3OZG5HJMTZLRWTKJRPSIRP","short_pith_number":"pith:Q7QO3OZG","schema_version":"1.0","canonical_sha256":"87e0edbb26e9d2c9e571b4d498be488bd6ba902936abf6e7180fb1a5bc7b63c0","source":{"kind":"arxiv","id":"2307.07947","version":1},"attestation_state":"computed","paper":{"title":"Language Conditioned Traffic Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Boris Ivanovic, Marco Pavone, Philipp Kraehenbuehl, Shuhan Tan, Xinshuo Weng","submitted_at":"2023-07-16T05:10:32Z","abstract_excerpt":"Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment at risk. However, simulators face a major challenge: They rely on realistic, scalable, yet interesting content. While recent advances in rendering and scene reconstruction make great strides in creating static scene assets, modeling their layout, dynamics, and behaviors remains challenging. In this work, we turn to language as a source of supervision for dynamic traffic scene generation. Our model, LCTGen, combines a"},"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":"2307.07947","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-07-16T05:10:32Z","cross_cats_sorted":[],"title_canon_sha256":"253260e241b94677f9a83da040420a637bc0d5358b8d64b7739b1ce040210d95","abstract_canon_sha256":"d18fdedeab81b82103fe3834f5412000b36f7bdbfe1889ab7b395aaa3451c54d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:31:18.148123Z","signature_b64":"Uf8DP5yXZloRqxK260tzQM2n8qanbTIJCRZ6NQRnxHzPUYxAqSiU59BonXX+LMAZKi9Y6jRzI3vQFkZQv3jOCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"87e0edbb26e9d2c9e571b4d498be488bd6ba902936abf6e7180fb1a5bc7b63c0","last_reissued_at":"2026-07-05T06:31:18.147716Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:31:18.147716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Language Conditioned Traffic Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Boris Ivanovic, Marco Pavone, Philipp Kraehenbuehl, Shuhan Tan, Xinshuo Weng","submitted_at":"2023-07-16T05:10:32Z","abstract_excerpt":"Simulation forms the backbone of modern self-driving development. Simulators help develop, test, and improve driving systems without putting humans, vehicles, or their environment at risk. However, simulators face a major challenge: They rely on realistic, scalable, yet interesting content. While recent advances in rendering and scene reconstruction make great strides in creating static scene assets, modeling their layout, dynamics, and behaviors remains challenging. In this work, we turn to language as a source of supervision for dynamic traffic scene generation. Our model, LCTGen, combines a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.07947","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/2307.07947/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":"2307.07947","created_at":"2026-07-05T06:31:18.147769+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.07947v1","created_at":"2026-07-05T06:31:18.147769+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.07947","created_at":"2026-07-05T06:31:18.147769+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q7QO3OZG5HJM","created_at":"2026-07-05T06:31:18.147769+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q7QO3OZG5HJMTZLR","created_at":"2026-07-05T06:31:18.147769+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q7QO3OZG","created_at":"2026-07-05T06:31:18.147769+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26533","citing_title":"OSC2Runner: OpenSCENARIO 2.x Compliant High-Fidelity AV Simulation in CARLA","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2606.19805","citing_title":"ParaScale: Scale-Calibrated Camera-Motion Transfer via a Gauge-Invariant Parallax Number","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2606.18950","citing_title":"RTSGameBench: An RTS Benchmark for Strategic Reasoning by Vision-Language Models","ref_index":48,"is_internal_anchor":false},{"citing_arxiv_id":"2503.20654","citing_title":"AccidentSim: Generating Vehicle Collision Videos with Physically Realistic Collision Trajectories from Real-World Accident Reports","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2507.01264","citing_title":"LLM-based Realistic Safety-Critical Driving Video Generation","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2604.07378","citing_title":"Evaluation as Evolution: Transforming Adversarial Diffusion into Closed-Loop Curricula for Autonomous Vehicles","ref_index":44,"is_internal_anchor":false},{"citing_arxiv_id":"2605.04366","citing_title":"Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Q7QO3OZG5HJMTZLRWTKJRPSIRP","json":"https://pith.science/pith/Q7QO3OZG5HJMTZLRWTKJRPSIRP.json","graph_json":"https://pith.science/api/pith-number/Q7QO3OZG5HJMTZLRWTKJRPSIRP/graph.json","events_json":"https://pith.science/api/pith-number/Q7QO3OZG5HJMTZLRWTKJRPSIRP/events.json","paper":"https://pith.science/paper/Q7QO3OZG"},"agent_actions":{"view_html":"https://pith.science/pith/Q7QO3OZG5HJMTZLRWTKJRPSIRP","download_json":"https://pith.science/pith/Q7QO3OZG5HJMTZLRWTKJRPSIRP.json","view_paper":"https://pith.science/paper/Q7QO3OZG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.07947&json=true","fetch_graph":"https://pith.science/api/pith-number/Q7QO3OZG5HJMTZLRWTKJRPSIRP/graph.json","fetch_events":"https://pith.science/api/pith-number/Q7QO3OZG5HJMTZLRWTKJRPSIRP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q7QO3OZG5HJMTZLRWTKJRPSIRP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q7QO3OZG5HJMTZLRWTKJRPSIRP/action/storage_attestation","attest_author":"https://pith.science/pith/Q7QO3OZG5HJMTZLRWTKJRPSIRP/action/author_attestation","sign_citation":"https://pith.science/pith/Q7QO3OZG5HJMTZLRWTKJRPSIRP/action/citation_signature","submit_replication":"https://pith.science/pith/Q7QO3OZG5HJMTZLRWTKJRPSIRP/action/replication_record"}},"created_at":"2026-07-05T06:31:18.147769+00:00","updated_at":"2026-07-05T06:31:18.147769+00:00"}