{"as_of":"2026-08-20T03:32:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e422da303f680ff3f05156ed2daf11174d0a223f3ce1c8ef433d649d535a67f1","coverage":[{"denominator":48,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":48,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T15:29:06.243172Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+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/2508.19866/citation-record","integrity":"/paper/2508.19866/integrity","json":"/paper/2508.19866/citation-record.json","paper":"/paper/2508.19866"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T15:29:07.216627Z","title":"Pedestrian de- tection: An evaluation of the state of the art,","venue":null,"work_id":"f26c907a-f917-4b11-88f1-589b8c2fd1f5","year":2011},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:05.956782Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:71420197a05c09ff358ad33035bdc981f66c4a2bed4b1881c4f870b31a1df792","observation_id":"1f68fb37-d06d-44c0-907b-5cff7617d427","resolution":{"observed_at":"2026-08-05T15:29:07.222304Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.198846Z","title":"Pedestrian protection systems: Issues, survey, and challenges,","venue":null,"work_id":"f1b0098c-0f6a-456b-8792-61951d953d2a","year":2007},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:05.963773Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:70593608c41920bc9789edd33f63112dbd2a473d62a4be35ff87ae421166d63c","observation_id":"00755072-2e78-4831-89bd-d185af7e9593","resolution":{"observed_at":"2026-08-05T15:29:07.204885Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.180103Z","title":"Pedestrian and vehicle be- haviour prediction in autonomous vehicle system—a review,","venue":null,"work_id":"97d27d99-5284-4daa-90c0-8c58c6df9fff","year":2023},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:05.969476Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:911044d595f8c5dffdd0e0b309830e84d7a0433de9f840c22bf3ff887a9188fd","observation_id":"b40a140f-1820-4d11-a938-a1bc4b7d7359","resolution":{"observed_at":"2026-08-05T15:29:07.186400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.161052Z","title":"Spatiotemporal relationship reasoning for pedestrian intent prediction,","venue":null,"work_id":"84b85449-8155-4913-987d-fc25c2e4564b","year":2020},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:05.975725Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:b01ff976c966dba687f17936130009736825b9a0ea47dab09b6b3e2c6097c5fa","observation_id":"37e3ca49-b687-492f-b5b9-46e3b2f80bb0","resolution":{"observed_at":"2026-08-05T15:29:07.166776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.143865Z","title":"Visual attention network,","venue":null,"work_id":"195a9556-2afd-4d4b-9bc0-bca3da063cc1","year":2023},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:05.982414Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:b7e94a35eabaff5aa263102392bbb83e6c47e670739774ba8a4175a4d5750948","observation_id":"fe341567-da40-4ed5-8887-553fb0fe66ba","resolution":{"observed_at":"2026-08-05T15:29:07.148841Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.125321Z","title":"Predicting pedestrian cross- ing intention in autonomous vehicles: A review,","venue":null,"work_id":"eec8ae55-728f-4af2-a535-ef3ae9cc0287","year":2025},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:05.988229Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:aca19d9982507524eee6ca68ec6727fdde6bfd20545d8347534dcafd281addc9","observation_id":"63bcf538-968a-484f-b888-84df4a250ff0","resolution":{"observed_at":"2026-08-05T15:29:07.131063Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.108070Z","title":"Pedestrian behavior prediction using deep learning methods for urban scenarios: A review,","venue":null,"work_id":"9f50538c-51e8-4a22-9d21-930858ab7a0c","year":2023},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:05.993866Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:ad5642e0dc1510bbd1d4bbc46400417d8bca60de42d0bdd33cf47f8a481b7980","observation_id":"e932dc58-27d9-4a34-a025-c65a4c2f89ad","resolution":{"observed_at":"2026-08-05T15:29:07.112996Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.090032Z","title":"Intention-aware pedestrian avoidance,","venue":null,"work_id":"b4f09671-3219-4c05-ad3f-3d43e2a6add1","year":2013},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:05.999971Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:124f857f27ffed031f5f144cd1bae7545bc038cb5b8e137df8f375d392018469","observation_id":"34dd4321-440a-47e3-a288-29cb8fbf0db4","resolution":{"observed_at":"2026-08-05T15:29:07.095496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.069255Z","title":"Context-based pedestrian path prediction,","venue":null,"work_id":"3d7b544b-af28-4396-a9af-564002f209b8","year":2014},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.007109Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:dc33622870c0acdebec786d23491d3f402003eec8624858fc0ea0c024e8c5c18","observation_id":"f37240df-3862-4eba-8d11-890c521c9ba6","resolution":{"observed_at":"2026-08-05T15:29:07.077262Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.046934Z","title":"Autonomous evasive maneuvers triggered by infrastructure-based detection of pedestrian intentions,","venue":null,"work_id":"ba396ea5-a330-4aea-87a3-6783196c4109","year":2013},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.013388Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:a2b43513fcce40e1f46375dfdb65181ac692a1815de229d64982744c94dd98d5","observation_id":"3d91d145-fa19-49ec-86e8-bcfae841503a","resolution":{"observed_at":"2026-08-05T15:29:07.054407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.030528Z","title":"Context-based detection of pedestrian crossing intention for autonomous driving in urban environments,","venue":null,"work_id":"42f8bf1b-0624-4933-9c63-a45bc6b94ad8","year":2016},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.018580Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:956b53efb9da636ba8e6c245d5c8bcdf51a0fdb4c4c4a7f925c038ad742017f9","observation_id":"b76f3014-5731-4053-8ac0-f82a4de6ea44","resolution":{"observed_at":"2026-08-05T15:29:07.035513Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:07.010034Z","title":"Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior,","venue":null,"work_id":"335d40d0-4db4-416b-b2df-1decfdc12af1","year":2017},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.023618Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:dd87ed39baba60bd2270d284b6aac3435a4e08674dab227914ff43aa9314f704","observation_id":"14fd117d-055f-429f-a72d-df3d179cbbda","resolution":{"observed_at":"2026-08-05T15:29:07.016167Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.992827Z","title":"Action and intention recognition of pedestrians in urban traffic,","venue":null,"work_id":"c4762aa6-1d61-4fb7-a25f-64cee3b54db7","year":2018},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.028114Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:59e5969f08cf6dc52aa53aded28c86b7c7fbf884fd0df94011dd331af3c1eb8e","observation_id":"e1e5b68c-8269-45db-9611-c16773fde5c0","resolution":{"observed_at":"2026-08-05T15:29:06.997874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.974920Z","title":"Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal densenet,","venue":null,"work_id":"2c496747-19ed-44e1-8119-d44d5bfd3310","year":2019},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.035212Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:2f3c68af19da7f255fe2c36f87439569f3ef7aa6a7432cb4c229a11d82c61578","observation_id":"5c1602aa-3231-41df-b15d-5b90cef7bf27","resolution":{"observed_at":"2026-08-05T15:29:06.981543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.956365Z","title":"Pedestrian motion state estimation from 2d pose,","venue":null,"work_id":"c288e5f9-7b9e-4f42-9552-10c5a35a4138","year":2020},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.039923Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:59cf58f2d4f2dda52f2e904d01a2de7bccc25402c8c3db0a0384b9663511a8e3","observation_id":"07050643-e8c1-459e-9a36-628db9e48dd6","resolution":{"observed_at":"2026-08-05T15:29:06.961147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.939434Z","title":"Rnn-based pedestrian crossing prediction using activity and pose-related features,","venue":null,"work_id":"95ba4373-09ad-445b-ac12-c837351d2e16","year":2020},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.048503Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:f6a23da17da51e2e27a6b2b7498e6ff4ecd92b872d8da3f65d5cc6dd2e4759fb","observation_id":"b19ab521-408b-475c-9076-b5a280f383e6","resolution":{"observed_at":"2026-08-05T15:29:06.944421Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.923558Z","title":"Do they want to cross? understanding pedestrian intention for behavior predic- tion,","venue":null,"work_id":"7826e753-f4a1-4463-adce-827d5f80fe37","year":2020},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.053938Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:e3ee77427edc14ea7005e1a0e2fa3205c8b369ab4501ecc35944580a6fb98017","observation_id":"caf4b26f-7f48-4186-9b34-5923c7d0c115","resolution":{"observed_at":"2026-08-05T15:29:06.928585Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.907484Z","title":"Vrunet: Multi-task learning model for intent prediction of vulnerable road users,","venue":null,"work_id":"a1929426-a351-47c1-9602-573def696351","year":2020},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.062739Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:81cf8ae80c0f0a2464d3554043716ee6fb2fc5b34c98ce729b1ee4feb3e9e10b","observation_id":"30311c57-a536-41b1-a6a5-4fcd10a46119","resolution":{"observed_at":"2026-08-05T15:29:06.912390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2005.06582","last_updated":"2020-05-13T20:59:37Z","snapshot_observed_at":"2026-08-10T13:07:54.040841Z","submitted_at":"2020-05-13T20:59:37Z","title":"Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs","version":1},"cited_work":{"arxiv_id":"2005.06582","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.06582","snapshot_observed_at":"2026-08-05T15:29:06.387431Z","title":"Pedestrian Action Anticipation using Contextual Feature Fusion in Stacked RNNs","venue":"cs.CV","work_id":"af31b840-dca1-4851-bf07-79eab53edcfb","year":2020},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.075114Z"},"links":{"cited_paper":"/paper/2005.06582","citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:d51f028fa5312e2b08f721fc3482771c936a3510230c28e9fd4406e3c0688243","observation_id":"c6f2f9a2-5bd1-4108-8993-6f8f3f143852","resolution":{"observed_at":"2026-08-05T15:29:06.393776Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.891808Z","title":"Benchmark for evaluating pedestrian action prediction,","venue":null,"work_id":"6a5cf5ac-045a-48df-b779-b542898f8e56","year":2021},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.081311Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:16376ee2fa6b3413f92e1af1384caf91bc9d5466c732d9d5b9d6c064c59df1a4","observation_id":"722d8d50-9fe5-4bd9-9548-77fdb9706d58","resolution":{"observed_at":"2026-08-05T15:29:06.896782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.865259Z","title":"Predicting pedestrian crossing intention with feature fusion and spatio-temporal attention,","venue":null,"work_id":"c7ff54b0-de8d-4211-ba50-522baea885d4","year":2022},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.091586Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:e2b8e3f839b54442b5f986798898ef6e9b4b85b1b19abd7211a9752d30319c1b","observation_id":"57defb89-24c4-41c2-bf31-d9d7ef56d1ea","resolution":{"observed_at":"2026-08-05T15:29:06.872216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.846196Z","title":"Trouspi-net: Spatio-temporal attention on parallel atrous con- volutions and u-grus for skeletal pedestrian crossing prediction,","venue":null,"work_id":"3550a963-1ee2-423c-850f-1b2ffe0bea7c","year":2021},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.096973Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:6aab5ed03a9ebe0cfeebef66582de615ea51f08e743f7cd4c14ca67046f06d38","observation_id":"f2313ad6-27b1-451d-982c-cf0f17ecc3c9","resolution":{"observed_at":"2026-08-05T15:29:06.851670Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.830367Z","title":"Attention is all you need,","venue":null,"work_id":"090d5c89-f703-4ed2-a098-7289477770f5","year":2017},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.102772Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:5cbd68f05c0614c83c799a1842e59a7b6b820311406906d091dae37b670b9181","observation_id":"a2ab97b4-8005-41b5-946c-4b6ed1796cf5","resolution":{"observed_at":"2026-08-05T15:29:06.834954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.811354Z","title":"Is attention to bounding boxes all you need for pedestrian ac- tion prediction?","venue":null,"work_id":"abf3862c-acb0-4f0a-800e-8cf5f7e349f8","year":2022},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.107464Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:d56e07c0f4c34204c406c72188dc382fb4beeae5db87c2f418e8da727beb6e5a","observation_id":"c894faca-1f2a-4c36-9f9f-10e6218bc04f","resolution":{"observed_at":"2026-08-05T15:29:06.816892Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.792775Z","title":"Pit: Progressive interaction transformer for pedestrian crossing intention predic- tion,","venue":null,"work_id":"2d610e37-e518-4828-bd4e-48e8b5b63725","year":2023},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.112912Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:a2157bf321e320a4732894acc6ee0cbf1f98959a2761c5e1f2ec1acf6c4c6af5","observation_id":"f0ac16ae-2db2-46ea-8c68-f479545fd9ee","resolution":{"observed_at":"2026-08-05T15:29:06.798275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.775959Z","title":"Deep virtual- to-real distillation for pedestrian crossing prediction,","venue":null,"work_id":"b390bff9-9ce0-4799-8b7c-8c1f771a111e","year":2022},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.117456Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:ccbd416313a5d91976d466fbae0066abf1d07a97e5c3b3ac5acab9d2e6cfd370","observation_id":"c413c831-c70e-4503-bc04-9913c8051231","resolution":{"observed_at":"2026-08-05T15:29:06.780750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.757902Z","title":"Capformer: Pedestrian crossing action prediction using transformer,","venue":null,"work_id":"66daf4dc-bda2-44d5-9d91-cf2fb93581a1","year":2021},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.122413Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:30a216de74c02d27c1c6f50f57f9cd3a5baf39e392af8711d156a04fca64ec57","observation_id":"861c825e-5323-41ce-8626-c470b5e5698f","resolution":{"observed_at":"2026-08-05T15:29:06.762745Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.741953Z","title":"Action-vit: Pedestrian intent prediction in traffic scenes,","venue":null,"work_id":"dc072779-b980-4e58-a572-34280af2fc15","year":2022},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.127548Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:3a873ce5a634f8df65babf6949f6f2e6376862428723088880a0ea300d735a11","observation_id":"a26e9109-a7b2-44d3-9576-910c792e21a4","resolution":{"observed_at":"2026-08-05T15:29:06.746990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.725971Z","title":"Classifying pedestrian actions in advance using predicted video of urban driving scenes,","venue":null,"work_id":"40b49601-b44a-4645-a407-82fe78fe8e79","year":2019},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.134961Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:daafff4a5a7ef821d7c8e7fe45cd9e732ee69963775e2797c8b10f6b601de491","observation_id":"6bb1cab6-2565-40cd-9642-7772f051d9c9","resolution":{"observed_at":"2026-08-05T15:29:06.730977Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.709862Z","title":"Looking ahead: Anticipating pedestrians crossing with future frames prediction,","venue":null,"work_id":"a32ebfa6-1e15-4efa-8053-40f9460094fa","year":2020},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.140664Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:b619e6f598810f868177af20ad5431b8b531f98b25cb3ab9de3770fd46194bed","observation_id":"f2366017-915a-4f60-98b3-543e39f1bce8","resolution":{"observed_at":"2026-08-05T15:29:06.715046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.694333Z","title":"Pedestrian intention prediction based on traffic-aware scene graph model,","venue":null,"work_id":"f9682a71-a467-46a9-82b1-224a4fa39418","year":2022},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.145393Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:11bde300f83ac81579b778ca20093ca3837716ed184921bac4204fa185c1b40e","observation_id":"6a6a546c-b542-4ef6-a73c-3140f211d9a2","resolution":{"observed_at":"2026-08-05T15:29:06.699331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.676859Z","title":"Pedestrian graph+: A fast pedestrian crossing prediction model based on graph convolutional networks,","venue":null,"work_id":"7548757c-822f-4b19-a9fc-281a87bc82ea","year":2022},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.153132Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:5c9e803fbefc84d6bb79e0b31b157fad8cc82e02d4a07a9f81995e8d6eb01808","observation_id":"41ea5b8b-c5cc-4bf3-b8d1-774436619066","resolution":{"observed_at":"2026-08-05T15:29:06.682101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.659456Z","title":"Dpcian: A novel dual-channel pedestrian crossing intention anticipation network,","venue":null,"work_id":"143e7d26-6343-47af-ac70-5b5cda6a193f","year":2024},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.157736Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:529182fb2aa929bbed6f86d0dfbe30d53197b17d863f83f785d4b70f68234f7d","observation_id":"6f6ba78f-2f01-4a89-9809-cafeceb9d58b","resolution":{"observed_at":"2026-08-05T15:29:06.664204Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.638201Z","title":"Pedast- gcn: Fast pedestrian crossing intention prediction using spatial– temporal attention graph convolution networks,","venue":null,"work_id":"37bfa0ef-492e-4e65-9c82-6e38c53127aa","year":2024},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.162343Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:41db48a3800f5cf7cc7901403e655bd3d68c401b8ceab769bbf345b25dfb2227","observation_id":"64d8d354-3ec0-4fd6-ae58-5c498a0a928d","resolution":{"observed_at":"2026-08-05T15:29:06.644903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.619443Z","title":"Feature selection and multi-task learning for pedestrian crossing prediction,","venue":null,"work_id":"d87ced01-21de-4a43-bdfd-0affc1678671","year":2022},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.168123Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:c9acc8c93bcb690ab420d49521bbe503b3eb76727a32ea90123b6542fd462f16","observation_id":"4e76f865-c224-4a11-b0db-4257dfd7349a","resolution":{"observed_at":"2026-08-05T15:29:06.624061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.598093Z","title":"Using graph convolutional networks skeleton-based pedestrian intention estimation models for tra- jectory prediction,","venue":null,"work_id":"4bc2ec03-47ec-4999-94ea-985f0039a71d","year":2020},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.173605Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:05884c62800273471aa9c51bb328dc2739a1a2a685afb1a3eefafe236f3ccb89","observation_id":"8a7884f3-f8aa-4374-bd73-d81c133cb520","resolution":{"observed_at":"2026-08-05T15:29:06.604629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.578353Z","title":"Joint inten- tion and trajectory prediction based on transformer,","venue":null,"work_id":"3cf08219-05c0-43a2-8e59-3adf0514e9e7","year":2021},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.180023Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:232928137c283bc226ef23e4dcfb8d3356b707e53ba633d817a250494e5e90f0","observation_id":"4f75cc90-c072-4b87-a3e5-62fddc6d3988","resolution":{"observed_at":"2026-08-05T15:29:06.583808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.562272Z","title":"Multi-task deep learning for pedestrian detection, action recognition and time to cross prediction,","venue":null,"work_id":"88f9ff4e-cb46-42b6-a0b6-cb7a12163e64","year":2019},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.190402Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:ef4546f30d927c832012b5024e511d12c2b9376ca396a8b5c0109907bf6270e8","observation_id":"2b11d4dc-4b42-487c-8d2c-e6b3a3d9a26c","resolution":{"observed_at":"2026-08-05T15:29:06.567826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.04133","last_updated":"2021-05-10T06:26:25Z","snapshot_observed_at":"2026-08-16T18:25:57.422455Z","submitted_at":"2021-05-10T06:26:25Z","title":"Coupling Intent and Action for Pedestrian Crossing Behavior Prediction","version":1},"cited_work":{"arxiv_id":"2105.04133","doi":null,"metadata_source":"pith","pith_arxiv_id":"2105.04133","snapshot_observed_at":"2026-08-05T15:29:06.331460Z","title":"Coupling Intent and Action for Pedestrian Crossing Behavior Prediction","venue":"cs.CV","work_id":"394ea101-351e-4ab2-be26-178f3abd1061","year":2021},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.195964Z"},"links":{"cited_paper":"/paper/2105.04133","citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:c6a31c720bb43edfd2cef4424be76493dc4b8309264021d243a06bcb60afab26","observation_id":"e9d59785-219d-4d21-9d08-71f308eb0f63","resolution":{"observed_at":"2026-08-05T15:29:06.344292Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.544173Z","title":"Social aware multi-modal pedestrian crossing behavior prediction,","venue":null,"work_id":"e1d21137-4f1d-405c-ae49-65ac538e90c8","year":2022},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.201226Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:8aa4fe7b834f708024f1a006fb1d19195d305ccb526e9164809f35e92185aeb1","observation_id":"29cd0149-b628-4c73-bb11-db73d90fc847","resolution":{"observed_at":"2026-08-05T15:29:06.549023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.524733Z","title":"Pie: A large-scale dataset and models for pedestrian intention estima- tion and trajectory prediction,","venue":null,"work_id":"a0127bb2-2da9-44a7-9bf2-c3db445bb997","year":2019},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.206778Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:61eae4f0ecfbba0abaa4c16ce314143ea929ba47528c001da7c829613b4d079c","observation_id":"2ae538c7-8c61-4eb6-8447-e6fededd3c88","resolution":{"observed_at":"2026-08-05T15:29:06.530756Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.13278","last_updated":"2026-05-04T08:07:42Z","snapshot_observed_at":"2026-08-12T17:39:58.835778Z","submitted_at":"2024-07-18T08:31:55Z","title":"Deep Time Series Models: A Comprehensive Survey and Benchmark","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.13278","snapshot_observed_at":"2026-08-05T15:29:06.211596Z","title":"Deep time series models: A comprehensive survey and benchmark,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.211596Z"},"links":{"cited_paper":"/paper/2407.13278","citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:e36dfde44c69cbb22c61acb89834d734c7f058a6b6cfb17e872814d834bf9d6d","observation_id":"f725f0ea-8905-4fdf-ba9b-f63d12993f4d","resolution":{"observed_at":"2026-08-05T15:29:06.211596Z","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":"2026-08-05T15:29:06.504003Z","title":"Scene parsing through ade20k dataset,","venue":null,"work_id":"366f95d6-d91a-4261-b1cd-a4e6c4fb2494","year":2017},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.217146Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:067630bec33b75ca529833132b4f55c07a04acd783fab6bf9d76b9f69e9dcb3c","observation_id":"6dd1915d-8b4c-4455-8c2f-97deffb3dd72","resolution":{"observed_at":"2026-08-05T15:29:06.509715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.222753Z","title":"A fast learning algorithm for deep belief nets,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.222753Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:248b3a74438705c51e49539d6ce3a686b1c60460c5d7907f30d8d6c00280bf2d","observation_id":"d92bda2b-efc7-4d66-a0f8-5159c2680c92","resolution":{"observed_at":"2026-08-05T15:29:06.222753Z","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":"2026-08-05T15:29:06.467133Z","title":"Greedy layer-wise training of deep networks,","venue":null,"work_id":"26527e9f-2310-49dc-8cab-d6824674f6e9","year":2006},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.228243Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:cff31ab079205e262e30bbf437a96db919f35ada34c671a7fb3b06238687b48b","observation_id":"9e937e37-4cb1-4ec9-b470-f4562043ca6a","resolution":{"observed_at":"2026-08-05T15:29:06.472496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.447770Z","title":"Stage-wise training: An improved feature learning strategy for deep models,","venue":null,"work_id":"52bdd437-23cf-49ef-907f-c8c3d6f1a9dd","year":2015},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.232930Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:963eca95c2fd2b7ce583bbdc08ae0197b2ef270ea22cd5e422cc6a799fcb3537","observation_id":"fa878e84-c37e-40b8-949a-d62a0a43ded3","resolution":{"observed_at":"2026-08-05T15:29:06.454355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.424718Z","title":"Realtime multi- person 2d pose estimation using part affinity fields,","venue":null,"work_id":"985f604a-8c28-47f4-9687-ff64897b5bb2","year":2017},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.237550Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:42905dc7e67bc143d0844eb59f4311c9748c2eb99a36375691b86adce0188ef3","observation_id":"56bd4d55-554b-4bcf-950e-a033d201640d","resolution":{"observed_at":"2026-08-05T15:29:06.431692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-05T15:29:06.407571Z","title":"Rethinking atrous convolution for semantic image seg- mentation,","venue":null,"work_id":"3cf506a5-8de5-4c01-ae1d-0c21cb09e3b7","year":2017},"citing_paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T15:29:06.243172Z"},"links":{"citing_paper":"/paper/2508.19866"},"observation_digest":"sha256:28b1dbc70f08a6fc38f25f1f44d29a124257e430c9214d11de492ffa987ea459","observation_id":"f568197b-99e0-42e9-922c-c4652d62ec00","resolution":{"observed_at":"2026-08-05T15:29:06.413254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.19866","last_updated":"2025-08-27T13:29:15Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T01:31:29.683351Z","submitted_at":"2025-08-27T13:29:15Z","title":"TrajFusionNet: Pedestrian Crossing Intention Prediction via Fusion of Sequential and Visual Trajectory Representations"},"reference_resolution":{"displayed":48,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":2,"verified_fuzzy":44},"total_outbound_references":48},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2508.19866."}