{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SOTPV3PFM2H2FG43DTSFEQHMJA","short_pith_number":"pith:SOTPV3PF","schema_version":"1.0","canonical_sha256":"93a6faede5668fa29b9b1ce45240ec4814cdd874d5e2722bb91fa0200f56a282","source":{"kind":"arxiv","id":"2503.12170","version":2},"attestation_state":"computed","paper":{"title":"DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Chang Huang, Cong Zhang, Kun Li, Tao Wang, Weiwei Liu, Xingguang Qu","submitted_at":"2025-03-15T15:23:35Z","abstract_excerpt":"End-to-end autonomous driving (E2E-AD) has rapidly emerged as a promising approach toward achieving full autonomy. However, existing E2E-AD systems typically adopt a traditional multi-task framework, addressing perception, prediction, and planning tasks through separate task-specific heads. Despite being trained in a fully differentiable manner, they still encounter issues with task coordination, and the system complexity remains high. In this work, we introduce DiffAD, a novel diffusion probabilistic model that redefines autonomous driving as a conditional image generation task. By rasterizin"},"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":"2503.12170","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.RO","submitted_at":"2025-03-15T15:23:35Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"11c238f897c3c3bd24cf3b0ec237caafd958dd36c7fbdfef721f49abdffc6664","abstract_canon_sha256":"420ef74563ec34bb1df8fc97b47d425af2b5d39f8f027284b36018dafb247806"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:10.244275Z","signature_b64":"KHi/HMKLh4+5psynOeX4DzL2tqZw42M1j0p7XrlEZi3LeUX5fX8Ztsk7o2OHSd8nTn9dQX76K9e8t9mZKi5pCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"93a6faede5668fa29b9b1ce45240ec4814cdd874d5e2722bb91fa0200f56a282","last_reissued_at":"2026-07-05T11:39:10.243759Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:10.243759Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiffAD: A Unified Diffusion Modeling Approach for Autonomous Driving","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.RO","authors_text":"Chang Huang, Cong Zhang, Kun Li, Tao Wang, Weiwei Liu, Xingguang Qu","submitted_at":"2025-03-15T15:23:35Z","abstract_excerpt":"End-to-end autonomous driving (E2E-AD) has rapidly emerged as a promising approach toward achieving full autonomy. However, existing E2E-AD systems typically adopt a traditional multi-task framework, addressing perception, prediction, and planning tasks through separate task-specific heads. Despite being trained in a fully differentiable manner, they still encounter issues with task coordination, and the system complexity remains high. In this work, we introduce DiffAD, a novel diffusion probabilistic model that redefines autonomous driving as a conditional image generation task. By rasterizin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.12170","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2503.12170/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":"2503.12170","created_at":"2026-07-05T11:39:10.243830+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.12170v2","created_at":"2026-07-05T11:39:10.243830+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.12170","created_at":"2026-07-05T11:39:10.243830+00:00"},{"alias_kind":"pith_short_12","alias_value":"SOTPV3PFM2H2","created_at":"2026-07-05T11:39:10.243830+00:00"},{"alias_kind":"pith_short_16","alias_value":"SOTPV3PFM2H2FG43","created_at":"2026-07-05T11:39:10.243830+00:00"},{"alias_kind":"pith_short_8","alias_value":"SOTPV3PF","created_at":"2026-07-05T11:39:10.243830+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.25736","citing_title":"UniTeD: Unified Temporal Diffusion for Joint Perception and Planning in Autonomous Driving","ref_index":53,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30807","citing_title":"Off the Rails: Hijacking the Scoring Head in Generative End-to-End Driving Planners with Safety-Violating Adversarial Perturbations","ref_index":49,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30421","citing_title":"OWMDrive: Causality-Aware End-to-End Autonomous Driving via 4D Occupancy World Model","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2505.16278","citing_title":"DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2507.04049","citing_title":"DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving","ref_index":39,"is_internal_anchor":false},{"citing_arxiv_id":"2601.01762","citing_title":"AlignDrive: Aligned Lateral-Longitudinal Planning for End-to-End Autonomous Driving","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.08719","citing_title":"LMGenDrive: Bridging Multimodal Understanding and Generative World Modeling for End-to-End Driving","ref_index":50,"is_internal_anchor":false},{"citing_arxiv_id":"2604.17147","citing_title":"ScenarioControl: Vision-Language Controllable Vectorized Latent Scenario Generation","ref_index":48,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SOTPV3PFM2H2FG43DTSFEQHMJA","json":"https://pith.science/pith/SOTPV3PFM2H2FG43DTSFEQHMJA.json","graph_json":"https://pith.science/api/pith-number/SOTPV3PFM2H2FG43DTSFEQHMJA/graph.json","events_json":"https://pith.science/api/pith-number/SOTPV3PFM2H2FG43DTSFEQHMJA/events.json","paper":"https://pith.science/paper/SOTPV3PF"},"agent_actions":{"view_html":"https://pith.science/pith/SOTPV3PFM2H2FG43DTSFEQHMJA","download_json":"https://pith.science/pith/SOTPV3PFM2H2FG43DTSFEQHMJA.json","view_paper":"https://pith.science/paper/SOTPV3PF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.12170&json=true","fetch_graph":"https://pith.science/api/pith-number/SOTPV3PFM2H2FG43DTSFEQHMJA/graph.json","fetch_events":"https://pith.science/api/pith-number/SOTPV3PFM2H2FG43DTSFEQHMJA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SOTPV3PFM2H2FG43DTSFEQHMJA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SOTPV3PFM2H2FG43DTSFEQHMJA/action/storage_attestation","attest_author":"https://pith.science/pith/SOTPV3PFM2H2FG43DTSFEQHMJA/action/author_attestation","sign_citation":"https://pith.science/pith/SOTPV3PFM2H2FG43DTSFEQHMJA/action/citation_signature","submit_replication":"https://pith.science/pith/SOTPV3PFM2H2FG43DTSFEQHMJA/action/replication_record"}},"created_at":"2026-07-05T11:39:10.243830+00:00","updated_at":"2026-07-05T11:39:10.243830+00:00"}