{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WC2SB5QV4FXDCAJZ6H4HCOSNPB","short_pith_number":"pith:WC2SB5QV","schema_version":"1.0","canonical_sha256":"b0b520f615e16e310139f1f8713a4d7856b06e0018c1d9491c40699a00b8121c","source":{"kind":"arxiv","id":"2502.01357","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dong-Hee Paek, Dong-In Kim, Seung-Hyun Kong, Seung-Hyun Song","submitted_at":"2025-02-03T13:49:21Z","abstract_excerpt":"Accurate 3D multi-object tracking (MOT) is vital for autonomous vehicles, yet LiDAR and camera-based methods degrade in adverse weather. Meanwhile, Radar-based solutions remain robust but often suffer from limited vertical resolution and simplistic motion models. Existing Kalman filter-based approaches also rely on fixed noise covariance, hampering adaptability when objects make sudden maneuvers. We propose Bayes-4DRTrack, a 4D Radar-based MOT framework that adopts a transformer-based motion prediction network to capture nonlinear motion dynamics and employs Bayesian approximation in both dete"},"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":"2502.01357","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-03T13:49:21Z","cross_cats_sorted":[],"title_canon_sha256":"db9a2a690792c3453d8dda2890aad22cfba259257ec72f09f0cb512776635320","abstract_canon_sha256":"eab0d70c6fbe8ffdf843d3183e83e7eb4761947c1934a4af77e7d7fbe8f54a59"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:46.183424Z","signature_b64":"U20/9pfuIHMymJeT8uUwyUpxEXDcBZgKMghG7sSDPxq4GU5nEKmAUv3pZoRsb7SpbMmeTqB4kWlKkYwFRfbVDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0b520f615e16e310139f1f8713a4d7856b06e0018c1d9491c40699a00b8121c","last_reissued_at":"2026-07-05T10:08:46.182945Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:46.182945Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dong-Hee Paek, Dong-In Kim, Seung-Hyun Kong, Seung-Hyun Song","submitted_at":"2025-02-03T13:49:21Z","abstract_excerpt":"Accurate 3D multi-object tracking (MOT) is vital for autonomous vehicles, yet LiDAR and camera-based methods degrade in adverse weather. Meanwhile, Radar-based solutions remain robust but often suffer from limited vertical resolution and simplistic motion models. Existing Kalman filter-based approaches also rely on fixed noise covariance, hampering adaptability when objects make sudden maneuvers. We propose Bayes-4DRTrack, a 4D Radar-based MOT framework that adopts a transformer-based motion prediction network to capture nonlinear motion dynamics and employs Bayesian approximation in both dete"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.01357","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/2502.01357/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":"2502.01357","created_at":"2026-07-05T10:08:46.183003+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.01357v1","created_at":"2026-07-05T10:08:46.183003+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.01357","created_at":"2026-07-05T10:08:46.183003+00:00"},{"alias_kind":"pith_short_12","alias_value":"WC2SB5QV4FXD","created_at":"2026-07-05T10:08:46.183003+00:00"},{"alias_kind":"pith_short_16","alias_value":"WC2SB5QV4FXDCAJZ","created_at":"2026-07-05T10:08:46.183003+00:00"},{"alias_kind":"pith_short_8","alias_value":"WC2SB5QV","created_at":"2026-07-05T10:08:46.183003+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WC2SB5QV4FXDCAJZ6H4HCOSNPB","json":"https://pith.science/pith/WC2SB5QV4FXDCAJZ6H4HCOSNPB.json","graph_json":"https://pith.science/api/pith-number/WC2SB5QV4FXDCAJZ6H4HCOSNPB/graph.json","events_json":"https://pith.science/api/pith-number/WC2SB5QV4FXDCAJZ6H4HCOSNPB/events.json","paper":"https://pith.science/paper/WC2SB5QV"},"agent_actions":{"view_html":"https://pith.science/pith/WC2SB5QV4FXDCAJZ6H4HCOSNPB","download_json":"https://pith.science/pith/WC2SB5QV4FXDCAJZ6H4HCOSNPB.json","view_paper":"https://pith.science/paper/WC2SB5QV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.01357&json=true","fetch_graph":"https://pith.science/api/pith-number/WC2SB5QV4FXDCAJZ6H4HCOSNPB/graph.json","fetch_events":"https://pith.science/api/pith-number/WC2SB5QV4FXDCAJZ6H4HCOSNPB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WC2SB5QV4FXDCAJZ6H4HCOSNPB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WC2SB5QV4FXDCAJZ6H4HCOSNPB/action/storage_attestation","attest_author":"https://pith.science/pith/WC2SB5QV4FXDCAJZ6H4HCOSNPB/action/author_attestation","sign_citation":"https://pith.science/pith/WC2SB5QV4FXDCAJZ6H4HCOSNPB/action/citation_signature","submit_replication":"https://pith.science/pith/WC2SB5QV4FXDCAJZ6H4HCOSNPB/action/replication_record"}},"created_at":"2026-07-05T10:08:46.183003+00:00","updated_at":"2026-07-05T10:08:46.183003+00:00"}