{"as_of":"2026-08-13T23:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9a3b10ed905204bcab93d47e94e6162e2bfb206d9de890338a25da803918d82e","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T15:38:35.177834Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.01357/citation-record","integrity":"/paper/2502.01357/integrity","json":"/paper/2502.01357/citation-record.json","paper":"/paper/2502.01357"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.948486Z","title":"Vehicle detection and tracking in adverse weather using a deep learning framework,","venue":null,"work_id":"c1fc55a6-dc54-4fe5-88ce-82a570a2e649","year":2020},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:34.904930Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:535858de10cda0a5fa02c326b93a0b1cda45c71da281ba48780dcf233948b2e6","observation_id":"4ae780e1-53ec-4c76-9c6a-b321ab77dd96","resolution":{"observed_at":"2026-08-09T15:38:35.953410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.933201Z","title":"K-radar: 4d radar object detection for autonomous driving in various weather conditions,","venue":null,"work_id":"9f2d56c1-679e-4a9d-8a08-e94c683dc938","year":2022},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:34.995282Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:baf2324421dfea3fbfe9ffa960e44bb59bf0ded10faca2496454eb82e1004a8f","observation_id":"0e1ac041-a816-437f-902b-4bd206e3909b","resolution":{"observed_at":"2026-08-09T15:38:35.938103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.918198Z","title":"Detection and tracking on automotive radar data with deep learning,","venue":null,"work_id":"ac79c89e-49f3-4e1f-9fa2-9370804a8a37","year":2020},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.000343Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:4234886716fca3d78ed35a3d685925209be3b5f4b743863d24b89759c422fef5","observation_id":"cc834557-3ecc-4a32-b0ca-06dd47d360d4","resolution":{"observed_at":"2026-08-09T15:38:35.923298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.903243Z","title":"Deep-learning based multi- object detection and tracking using range-angle map in automo- tive radar systems,","venue":null,"work_id":"09e38577-c6d4-4272-ab55-11814f2a2a28","year":2022},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.005474Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:640d22cf510502b22cf4ce7219a28f502a42343a6a917874d3dd80ccdabfc961","observation_id":"70636622-db7a-465d-aa66-3aa5dd887ae8","resolution":{"observed_at":"2026-08-09T15:38:35.907822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.888614Z","title":"Tj4dradset: A 4d radar dataset for au- tonomous driving,","venue":null,"work_id":"05672843-8e38-4ebd-9f9e-69d5a8ca52a0","year":2022},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.010902Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:be4834fcf5f1b7779a07a910ea020f932b6d9f0deb6dc402d72409b68c605404","observation_id":"2894bca1-4481-468c-a8db-d43a6b179bb4","resolution":{"observed_at":"2026-08-09T15:38:35.893214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.015727Z","title":"Multi- class road user detection with 3+ 1d radar in the view-of-delft dataset,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.015727Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:6faf67e5a051d6e8f4c31a0ae79a0829ec86e91fd45b167c23165047b56be425","observation_id":"831e1551-e142-448f-b6e2-423cd24a0910","resolution":{"observed_at":"2026-08-09T15:38:35.015727Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.864091Z","title":"Enhanced k-radar: Optimal density reduction to improve detection performance and accessibility of 4d radar tensor-based object detection,","venue":null,"work_id":"6c45ce7d-f205-4e83-b97c-9934ec97068f","year":2023},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.021000Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:623ffec23dc9cf0a651c8e22014051de57c34818cd68b6f77b5f9f6a9b0715af","observation_id":"bd2e71a8-4cd5-4b65-b7b7-f83495faa5ad","resolution":{"observed_at":"2026-08-09T15:38:35.868930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.025952Z","title":"A new approach to linear filtering and prediction problems,","venue":null,"work_id":null,"year":1960},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.025952Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:8036e44a31141dd45c3cdca7c3cc916241b06bb84d752342526db032b1b51a80","observation_id":"2a37b5b6-2f29-4431-9627-0c3deac695af","resolution":{"observed_at":"2026-08-09T15:38:35.025952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.030520Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.030520Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:5f3e80cf0100fd9c4bc81a603f4dfe596516c2d18ccb3ed286486cd7c666f291","observation_id":"b77997f6-5432-4578-b362-7610b5beace8","resolution":{"observed_at":"2026-08-09T15:38:35.030520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.035240Z","title":"3d multi-object tracking: A baseline and new evaluation metrics,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.035240Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:d2ebde549baad4f3595f7eff49d33c06fc64dae3e9ce1cfa7c1fc103e3976369","observation_id":"6227c3bc-f602-40ec-946c-4b60c42a81a8","resolution":{"observed_at":"2026-08-09T15:38:35.035240Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.817265Z","title":"Probabilistic 3d multi- modal, multi-object tracking for autonomous driving,","venue":null,"work_id":"d35a28f7-9577-40ab-bfe0-13e16d9576ab","year":2021},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.039756Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:b4fe2d4480dc1e06a03e7ebcef1cb27e8a1f5d8cd818f93ed22fc3820e7d4a17","observation_id":"fcc43094-e498-4e04-9475-01ed1d90dc2e","resolution":{"observed_at":"2026-08-09T15:38:35.822613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.044191Z","title":"Simpletrack: Understanding and rethinking 3d multi-object tracking,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.044191Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:4cf7f14606a7f12acede7812f51a4062c8d6184d64ebc87ac273963dd13ba4ef","observation_id":"63b0500a-04e7-48a8-bd7f-76b2e46635e0","resolution":{"observed_at":"2026-08-09T15:38:35.044191Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.749124Z","title":"Score refinement for confidence-based 3d multi-object tracking,","venue":null,"work_id":"ca192279-5d39-424e-a17b-20124acd777a","year":2021},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.050203Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:67ed29ec951b44e8aff911f56729a200ce70779e76cb4d27a304d386b0beccea","observation_id":"a61b56da-a8b5-4c1d-b3cc-fca7f3fe639e","resolution":{"observed_at":"2026-08-09T15:38:35.773399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.054514Z","title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.054514Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:caf05c0baf3cd5ec1c09e96ea90510c4bd7ab2238a76a3b8b3344d7cb4c2db3e","observation_id":"3969aa50-b5a5-43e8-b048-f8f27430d0ff","resolution":{"observed_at":"2026-08-09T15:38:35.054514Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.058715Z","title":"What uncertainties do we need in bayesian deep learning for computer vision?","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.058715Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:ab73b03554e7f087821930f84127b94e10d5442fe13ac66aa7fff64605be8912","observation_id":"26f07193-858a-46a7-89d9-cc07da5c75d8","resolution":{"observed_at":"2026-08-09T15:38:35.058715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.062873Z","title":"Center-based 3d object detec- tion and tracking,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.062873Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:7980996918c5c9b248429828a6befad0dd2add808eda6915cedf38ff6884c9f0","observation_id":"6825e952-e487-42f0-bb30-afb5ea86399d","resolution":{"observed_at":"2026-08-09T15:38:35.062873Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.586514Z","title":"3dmodt: Attention-guided affinities for joint detection & tracking in 3d point clouds,","venue":null,"work_id":"d1363a51-f3e2-4d69-b77e-e30c6c6a27ac","year":2023},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.067625Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:941e1bab858ef77dfdd765e3f89228770f13b91e3008e0359413bae21a7a0348","observation_id":"fa397707-3127-4b6b-9808-4e41b1641d53","resolution":{"observed_at":"2026-08-09T15:38:35.624636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.537001Z","title":"Monocular quasi-dense 3d object tracking,","venue":null,"work_id":"080bbca4-ce9c-4e0d-9625-0cfdf0bf9190","year":1992},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.072743Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:73caa667e72b56cf8038f91827b57c1fea2495d89d8c304b632e09702ab3444d","observation_id":"b7c35c6f-82ec-4329-89d3-575cdaf61b79","resolution":{"observed_at":"2026-08-09T15:38:35.542000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.01247","last_updated":"2022-12-02T15:43:55Z","snapshot_observed_at":"2026-08-13T13:29:18.830808Z","submitted_at":"2022-12-02T15:43:55Z","title":"CC-3DT: Panoramic 3D Object Tracking via Cross-Camera Fusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.01247","snapshot_observed_at":"2026-08-09T15:38:35.077551Z","title":"Cc-3dt: Panoramic 3d object tracking via cross-camera fusion,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.077551Z"},"links":{"cited_paper":"/paper/2212.01247","citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:d43480ccb42bbe8e24c73ee7c901ff6a363ce78a9cf20118a68734543fc5024d","observation_id":"cda252a7-e625-499c-be24-3d2ad2ea73e2","resolution":{"observed_at":"2026-08-09T15:38:35.077551Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.521596Z","title":"Mutr3d: A multi-camera tracking framework via 3d-to-2d queries,","venue":null,"work_id":"64dc8b7b-95e1-41ce-84b6-8a69aebb0fe9","year":2022},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.082649Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:0bc453c04c214238bd60f3eecdc6409f7b876bea40ff2bcdc4280dd96e0111d9","observation_id":"7a30fae8-a92c-4071-978f-e51608761625","resolution":{"observed_at":"2026-08-09T15:38:35.526350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.506121Z","title":"Standing between past and future: Spatio-temporal modeling for multi-camera 3d multi-object tracking,","venue":null,"work_id":"284fc3d5-f9b0-457c-b028-99a8dd88d756","year":2023},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.087266Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:d7c46435bc6974e50e6e4179664c86acfa6753e79f9f3d8bf5ddd1b7a15489ca","observation_id":"37e5f7cd-f123-46cd-bb5d-65040c1ae24a","resolution":{"observed_at":"2026-08-09T15:38:35.511388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.091574Z","title":"Gnn3dmot: Graph neural network for 3d multi-object tracking with 2d-3d multi-feature learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.091574Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:1626a961547efda0ea3702026dfa5c9ed74282685f593fa17fc5b2b2aecae55c","observation_id":"018740c8-abcd-4ecc-bf41-cde124649600","resolution":{"observed_at":"2026-08-09T15:38:35.091574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.481379Z","title":"Joint multi-object detection and tracking with camera-lidar fusion for autonomous driving,","venue":null,"work_id":"c2e564e7-1b5f-4257-a05d-1b6f03a617e8","year":2021},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.095697Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:467e9fc084cf272e3c62e60bb9a22cb19b0d55ff18b6d242f05aba0428d52a5f","observation_id":"764bfcb5-ca8a-40c5-b65a-871305785cc8","resolution":{"observed_at":"2026-08-09T15:38:35.486259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.463760Z","title":"Cross-modal 3d object detection and tracking for auto-driving,","venue":null,"work_id":"2d032ba5-90a0-45f9-bb42-89803dfb803c","year":2021},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.100164Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:dc102360a05b25d6e703748488b5a9cd0148d289207c81c8283977cce1d138d6","observation_id":"a119a2ee-8dd8-4b77-a689-b2d9911e9760","resolution":{"observed_at":"2026-08-09T15:38:35.469329Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.104627Z","title":"Simple and scalable predictive uncertainty estimation using deep ensembles,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.104627Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:7988f2f1c4230b3c509349a1407d53cef29c52c0f4ce50f6c190203bc63d4b58","observation_id":"4bd736df-d529-412c-835d-10f78ab81044","resolution":{"observed_at":"2026-08-09T15:38:35.104627Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.438111Z","title":"Deep multi-view depth estimation with predicted uncertainty,","venue":null,"work_id":"07a879e7-394d-4353-b154-9296d9899ad9","year":2021},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.109103Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:29d926d912df22f7fe4c27770a21acc970f1c36a019263da3f253907516aaae7","observation_id":"740c0a25-bdbd-45e4-a183-8a4ec37649a6","resolution":{"observed_at":"2026-08-09T15:38:35.442983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.417538Z","title":"Stochas- tic segmentation networks: Modelling spatially correlated aleatoric un- certainty,","venue":null,"work_id":"c16eb5e6-8665-4a3f-81b9-f21f23863713","year":2020},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.113674Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:9e4bdfe9bf54c81405521612407e45ff43f629d91cadffb5317073c058bac2fc","observation_id":"383abf1a-1baf-4481-af04-063a1383ede3","resolution":{"observed_at":"2026-08-09T15:38:35.423098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.398432Z","title":"Towards safe autonomous driving: Capture uncertainty in the deep neural network for lidar 3d vehicle detection,","venue":null,"work_id":"908ef73f-0dbc-420a-b225-6206bb2b958f","year":2018},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.117843Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:853e3d0ddcf8d67b142a38d64473753dbc1fa8ff9ef3369f57e8d67ca0e5552f","observation_id":"d826de13-1828-4787-b6f8-96b2930c1532","resolution":{"observed_at":"2026-08-09T15:38:35.403541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.383281Z","title":"Capturing object detection uncertainty in multi-layer grid maps,","venue":null,"work_id":"103242f4-453d-42e9-9a98-a5b49464e2de","year":2019},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.122153Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:9e1d8dd1a0a67dd4f06c88875d8b27a29867ae763d16f3a8521ebc8b659b0608","observation_id":"d53fc3a0-cb7d-41d0-be0e-b2591a35baf9","resolution":{"observed_at":"2026-08-09T15:38:35.387796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.368005Z","title":"Model uncertainty guides visual object tracking,","venue":null,"work_id":"24d6ce83-e9ed-44db-8305-0e0dec94f55c","year":2021},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.127737Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:b3c57de3da56aa8b9780e16ebdcd1da4c43d92962a1ebde437777d6fdb077bbd","observation_id":"3461b4b5-2e06-46f4-8f84-76e6a3c8f287","resolution":{"observed_at":"2026-08-09T15:38:35.372971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.336652Z","title":"A review and comparative study on probabilistic object detection in autonomous driving,","venue":null,"work_id":"b3eebe55-0b1b-4ebc-8c46-a81b60d7d4ed","year":2021},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.162285Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:4364b234094ccd633a4e0e6becd592fb678194c5557d6f1151ac66a1f97fd837","observation_id":"8c8e57fb-4458-468b-a143-87cb4d260b53","resolution":{"observed_at":"2026-08-09T15:38:35.356939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T15:38:35.265960Z","title":"Exploiting the circulant structure of tracking-by-detection with kernels,","venue":null,"work_id":"740cfe49-41b0-4177-8d4d-f6113bd2de89","year":2012},"citing_paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T15:38:35.177834Z"},"links":{"citing_paper":"/paper/2502.01357"},"observation_digest":"sha256:80331b4f97cdb5eb24268e871385226bd81bced5ed75432a0e6a9b02683477d0","observation_id":"3bfc2d48-28a1-4568-9550-2f92c92e883c","resolution":{"observed_at":"2026-08-09T15:38:35.298264Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.01357","last_updated":"2025-02-03T13:49:21Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T22:55:10.165069Z","submitted_at":"2025-02-03T13:49:21Z","title":"Bayesian Approximation-Based Trajectory Prediction and Tracking with 4D Radar"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":21},"total_outbound_references":32},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2502.01357."}