{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ORXYDFB47VVBUXTKEFM366HGJG","short_pith_number":"pith:ORXYDFB4","schema_version":"1.0","canonical_sha256":"746f81943cfd6a1a5e6a2159bf78e6498a7c5cd42455e908a088e2bf4ff20f8d","source":{"kind":"arxiv","id":"2303.04302","version":1},"attestation_state":"computed","paper":{"title":"Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Felipe Manfio Barbosa, Fernando Santos Os\\'orio","submitted_at":"2023-03-08T00:48:32Z","abstract_excerpt":"One of the main paths towards the reduction of traffic accidents is the increase in vehicle safety through driver assistance systems or even systems with a complete level of autonomy. In these types of systems, tasks such as obstacle detection and segmentation, especially the Deep Learning-based ones, play a fundamental role in scene understanding for correct and safe navigation. Besides that, the wide variety of sensors in vehicles nowadays provides a rich set of alternatives for improvement in the robustness of perception in challenging situations, such as navigation under lighting and weath"},"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":"2303.04302","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-03-08T00:48:32Z","cross_cats_sorted":["cs.AI","cs.RO"],"title_canon_sha256":"1771ab3c480b02c0c4c9e32bdb2ff98cee7bbcd10a3f37f2a4f21e502e1fa84c","abstract_canon_sha256":"22617e1f600623a442a4f43d8ded67788b38eae7db1865aaace31e03de03686d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:49:19.643180Z","signature_b64":"Sa8PiwyLDv2+MHvvpwl9YKZWp5MaGKBHGeVLbIm1vQdDv4FNJr2V/Qe8PKmwCwM/OKVrMZCSyTCpAnZCveSZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"746f81943cfd6a1a5e6a2159bf78e6498a7c5cd42455e908a088e2bf4ff20f8d","last_reissued_at":"2026-07-05T05:49:19.642689Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:49:19.642689Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.RO"],"primary_cat":"cs.CV","authors_text":"Felipe Manfio Barbosa, Fernando Santos Os\\'orio","submitted_at":"2023-03-08T00:48:32Z","abstract_excerpt":"One of the main paths towards the reduction of traffic accidents is the increase in vehicle safety through driver assistance systems or even systems with a complete level of autonomy. In these types of systems, tasks such as obstacle detection and segmentation, especially the Deep Learning-based ones, play a fundamental role in scene understanding for correct and safe navigation. Besides that, the wide variety of sensors in vehicles nowadays provides a rich set of alternatives for improvement in the robustness of perception in challenging situations, such as navigation under lighting and weath"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.04302","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/2303.04302/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":"2303.04302","created_at":"2026-07-05T05:49:19.642750+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.04302v1","created_at":"2026-07-05T05:49:19.642750+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.04302","created_at":"2026-07-05T05:49:19.642750+00:00"},{"alias_kind":"pith_short_12","alias_value":"ORXYDFB47VVB","created_at":"2026-07-05T05:49:19.642750+00:00"},{"alias_kind":"pith_short_16","alias_value":"ORXYDFB47VVBUXTK","created_at":"2026-07-05T05:49:19.642750+00:00"},{"alias_kind":"pith_short_8","alias_value":"ORXYDFB4","created_at":"2026-07-05T05:49:19.642750+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.24044","citing_title":"CLLAP: Contrastive Learning-based LiDAR-Augmented Pretraining for Enhanced Radar-Camera Fusion","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ORXYDFB47VVBUXTKEFM366HGJG","json":"https://pith.science/pith/ORXYDFB47VVBUXTKEFM366HGJG.json","graph_json":"https://pith.science/api/pith-number/ORXYDFB47VVBUXTKEFM366HGJG/graph.json","events_json":"https://pith.science/api/pith-number/ORXYDFB47VVBUXTKEFM366HGJG/events.json","paper":"https://pith.science/paper/ORXYDFB4"},"agent_actions":{"view_html":"https://pith.science/pith/ORXYDFB47VVBUXTKEFM366HGJG","download_json":"https://pith.science/pith/ORXYDFB47VVBUXTKEFM366HGJG.json","view_paper":"https://pith.science/paper/ORXYDFB4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.04302&json=true","fetch_graph":"https://pith.science/api/pith-number/ORXYDFB47VVBUXTKEFM366HGJG/graph.json","fetch_events":"https://pith.science/api/pith-number/ORXYDFB47VVBUXTKEFM366HGJG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ORXYDFB47VVBUXTKEFM366HGJG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ORXYDFB47VVBUXTKEFM366HGJG/action/storage_attestation","attest_author":"https://pith.science/pith/ORXYDFB47VVBUXTKEFM366HGJG/action/author_attestation","sign_citation":"https://pith.science/pith/ORXYDFB47VVBUXTKEFM366HGJG/action/citation_signature","submit_replication":"https://pith.science/pith/ORXYDFB47VVBUXTKEFM366HGJG/action/replication_record"}},"created_at":"2026-07-05T05:49:19.642750+00:00","updated_at":"2026-07-05T05:49:19.642750+00:00"}