{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KJHBQE6HH3EJ22OYRI3O2RMMMM","short_pith_number":"pith:KJHBQE6H","schema_version":"1.0","canonical_sha256":"524e1813c73ec89d69d88a36ed458c631daf4718d29bacec73082f362cc1fc60","source":{"kind":"arxiv","id":"2412.09951","version":2},"attestation_state":"computed","paper":{"title":"WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Lv, Hao Chen, Songyan Zhang, Wenhui Huang, Zihui Gao","submitted_at":"2024-12-13T08:14:24Z","abstract_excerpt":"The emergence of general human knowledge and impressive logical reasoning capacity in rapidly progressed vision-language models (VLMs) have driven increasing interest in applying VLMs to high-level autonomous driving tasks, such as scene understanding and decision-making. However, an in-depth study on the relationship between knowledge proficiency, especially essential driving expertise, and closed-loop autonomous driving performance requires further exploration. In this paper, we investigate the effects of the depth and breadth of fundamental driving knowledge on closed-loop trajectory planni"},"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":"2412.09951","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-13T08:14:24Z","cross_cats_sorted":[],"title_canon_sha256":"ef45c04f4957e8e20b57cc4f7bb3bb8f7242c296600478a1efbf3b003b8ebb46","abstract_canon_sha256":"0fb6bf7f5b78cb27e40a18036f1d26796615fee9c1ccee75684d2c6e2276d3c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:08.554192Z","signature_b64":"fad1FnDWeajAXDEZq4gNXQ6LJf6ifnlJDgl/z2D3t9NVrKJy68s+HTzpxsdKhFMHRnL2n6C/6jzKAGDXE+UEBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"524e1813c73ec89d69d88a36ed458c631daf4718d29bacec73082f362cc1fc60","last_reissued_at":"2026-07-05T09:50:08.553724Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:08.553724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"WiseAD: Knowledge Augmented End-to-End Autonomous Driving with Vision-Language Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chen Lv, Hao Chen, Songyan Zhang, Wenhui Huang, Zihui Gao","submitted_at":"2024-12-13T08:14:24Z","abstract_excerpt":"The emergence of general human knowledge and impressive logical reasoning capacity in rapidly progressed vision-language models (VLMs) have driven increasing interest in applying VLMs to high-level autonomous driving tasks, such as scene understanding and decision-making. However, an in-depth study on the relationship between knowledge proficiency, especially essential driving expertise, and closed-loop autonomous driving performance requires further exploration. In this paper, we investigate the effects of the depth and breadth of fundamental driving knowledge on closed-loop trajectory planni"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.09951","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/2412.09951/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":"2412.09951","created_at":"2026-07-05T09:50:08.553788+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.09951v2","created_at":"2026-07-05T09:50:08.553788+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.09951","created_at":"2026-07-05T09:50:08.553788+00:00"},{"alias_kind":"pith_short_12","alias_value":"KJHBQE6HH3EJ","created_at":"2026-07-05T09:50:08.553788+00:00"},{"alias_kind":"pith_short_16","alias_value":"KJHBQE6HH3EJ22OY","created_at":"2026-07-05T09:50:08.553788+00:00"},{"alias_kind":"pith_short_8","alias_value":"KJHBQE6H","created_at":"2026-07-05T09:50:08.553788+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":8,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12396","citing_title":"VLGA: Vision-Language-Geometry-Action Models for Autonomous Driving","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01624","citing_title":"What to Test Next: Interpretable Coverage Gap Discovery in Driving VLMs","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.20082","citing_title":"VL-DPO: Vision-Language-Guided Finetuning for Preference-Aligned Autonomous Driving","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2506.13757","citing_title":"AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning","ref_index":81,"is_internal_anchor":false},{"citing_arxiv_id":"2604.00813","citing_title":"DVGT-2: Vision-Geometry-Action Model for Autonomous Driving at Scale","ref_index":84,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10564","citing_title":"DeepSight: Long-Horizon World Modeling via Latent States Prediction for End-to-End Autonomous Driving","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22260","citing_title":"Towards Safe Mobility: A Unified Transportation Foundation Model enabled by Open-Ended Vision-Language Dataset","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04857","citing_title":"The Blind Spot of Adaptation: Quantifying and Mitigating Forgetting in Fine-tuned Driving Models","ref_index":57,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KJHBQE6HH3EJ22OYRI3O2RMMMM","json":"https://pith.science/pith/KJHBQE6HH3EJ22OYRI3O2RMMMM.json","graph_json":"https://pith.science/api/pith-number/KJHBQE6HH3EJ22OYRI3O2RMMMM/graph.json","events_json":"https://pith.science/api/pith-number/KJHBQE6HH3EJ22OYRI3O2RMMMM/events.json","paper":"https://pith.science/paper/KJHBQE6H"},"agent_actions":{"view_html":"https://pith.science/pith/KJHBQE6HH3EJ22OYRI3O2RMMMM","download_json":"https://pith.science/pith/KJHBQE6HH3EJ22OYRI3O2RMMMM.json","view_paper":"https://pith.science/paper/KJHBQE6H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.09951&json=true","fetch_graph":"https://pith.science/api/pith-number/KJHBQE6HH3EJ22OYRI3O2RMMMM/graph.json","fetch_events":"https://pith.science/api/pith-number/KJHBQE6HH3EJ22OYRI3O2RMMMM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KJHBQE6HH3EJ22OYRI3O2RMMMM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KJHBQE6HH3EJ22OYRI3O2RMMMM/action/storage_attestation","attest_author":"https://pith.science/pith/KJHBQE6HH3EJ22OYRI3O2RMMMM/action/author_attestation","sign_citation":"https://pith.science/pith/KJHBQE6HH3EJ22OYRI3O2RMMMM/action/citation_signature","submit_replication":"https://pith.science/pith/KJHBQE6HH3EJ22OYRI3O2RMMMM/action/replication_record"}},"created_at":"2026-07-05T09:50:08.553788+00:00","updated_at":"2026-07-05T09:50:08.553788+00:00"}