{"paper":{"title":"Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Standardized US license plates serve as passive fiducial markers for accurate monocular vehicle distance estimation without training data.","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Manognya Lokesh Reddy, Zheng Liu","submitted_at":"2026-04-14T03:27:44Z","abstract_excerpt":"Accurate inter-vehicle distance estimation is a cornerstone of Advanced Driver Assistance Systems (ADAS) and autonomous driving. While LiDAR and radar provide high precision, their high cost prohibits widespread adoption in mass-market vehicles. Monocular camera-based estimation offers a low-cost alternative but suffers from fundamental scale ambiguity. Recent deep learning methods for monocular depth achieve impressive results yet require expensive supervised training, suffer from domain shift, and produce predictions that are difficult to certify for safety-critical deployment. This paper pr"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Extensive outdoor experiments confirm a mean absolute error of 2.3% at 10 m and continuous distance output during brief plate occlusions, outperforming deep learning baselines by a factor of five in relative error.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The standardized typography and dimensions of United States license plates can be reliably detected and measured as passive fiducial markers across the full range of automotive lighting and ambient conditions using the described four-method detector and three-stage identifier.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A training-free monocular system uses US license plate typography and dimensions as fiducial markers to achieve 2.3% mean absolute error at 10 m for vehicle distance estimation via geometric priors and hybrid fusion.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Standardized US license plates serve as passive fiducial markers for accurate monocular vehicle distance estimation without training data.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"df3abcadd5b994c6e958d8f7bf1d1e92b9df7602945381263e14dc6053544cc5"},"source":{"id":"2604.12239","kind":"arxiv","version":2},"verdict":{"id":"7b23eaf1-4736-460d-8a9a-cb48db579cbf","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T15:05:04.076350Z","strongest_claim":"Extensive outdoor experiments confirm a mean absolute error of 2.3% at 10 m and continuous distance output during brief plate occlusions, outperforming deep learning baselines by a factor of five in relative error.","one_line_summary":"A training-free monocular system uses US license plate typography and dimensions as fiducial markers to achieve 2.3% mean absolute error at 10 m for vehicle distance estimation via geometric priors and hybrid fusion.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The standardized typography and dimensions of United States license plates can be reliably detected and measured as passive fiducial markers across the full range of automotive lighting and ambient conditions using the described four-method detector and three-stage identifier.","pith_extraction_headline":"Standardized US license plates serve as passive fiducial markers for accurate monocular vehicle distance estimation without training data."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.12239/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"}