{"paper":{"title":"Ray-Based Simulation of Scattering from Discretized Curved Bodies for Vehicular and ISAC Applications","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Discretizing curved surfaces with facets sized by local curvature and wavelength, plus extended diffraction, improves ray-tracing accuracy for vehicle scattering predictions.","cross_cats":[],"primary_cat":"eess.SP","authors_text":"Ainur Ziganshin, Christian Schneider, Enrico M. Vitucci, Reiner Thomae, Vittorio Degli-Esposti, Wim Kotterman","submitted_at":"2026-04-07T15:18:05Z","abstract_excerpt":"Realistic modeling of scattering from curved metallic bodies - such as vehicles and roadside structures - is essential for cellular and vehicular channel modeling as well as radar applications. A practical approach is to approximate curved surfaces with planar facets and apply ray-tracing with diffraction methods; however, accuracy depends critically on both geometric discretization and diffraction modeling. This work investigates ray-tracing-based modeling of near-field scattering from curved bodies, both in the backscattering and in the forward (shadow) region; in the ray-tracing tool, diffr"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Results show that appropriate discretization combined with extended diffraction modeling significantly improves scattering prediction from curved bodies, providing a computationally efficient framework for vehicular propagation and integrated sensing and communication (ISAC) channel modeling.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that linking facet size to local curvature and wavelength balances geometric fidelity, computational accuracy and efficiency sufficiently for practical use in complex scenarios like vehicles.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Discretizing curved bodies with curvature- and wavelength-dependent facet sizes and using extended UTD improves ray-based scattering predictions for vehicles and roadside structures.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Discretizing curved surfaces with facets sized by local curvature and wavelength, plus extended diffraction, improves ray-tracing accuracy for vehicle scattering predictions.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d85277144823c300fd07d5a6991b3900f5c37ee907cd94d0b9fad82f7c85b0c5"},"source":{"id":"2604.05991","kind":"arxiv","version":2},"verdict":{"id":"2bc54abf-9c28-4233-8002-c4fcebfa83de","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T18:43:43.750533Z","strongest_claim":"Results show that appropriate discretization combined with extended diffraction modeling significantly improves scattering prediction from curved bodies, providing a computationally efficient framework for vehicular propagation and integrated sensing and communication (ISAC) channel modeling.","one_line_summary":"Discretizing curved bodies with curvature- and wavelength-dependent facet sizes and using extended UTD improves ray-based scattering predictions for vehicles and roadside structures.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that linking facet size to local curvature and wavelength balances geometric fidelity, computational accuracy and efficiency sufficiently for practical use in complex scenarios like vehicles.","pith_extraction_headline":"Discretizing curved surfaces with facets sized by local curvature and wavelength, plus extended diffraction, improves ray-tracing accuracy for vehicle scattering predictions."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.05991/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"}