{"as_of":"2026-08-13T12:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4820d63a609dff64712704907b6a4eaa25695195d861d3011cd66a68e5312c4a","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T16:40:03.893730Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"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/2411.13302/citation-record","integrity":"/paper/2411.13302/integrity","json":"/paper/2411.13302/citation-record.json","paper":"/paper/2411.13302"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T16:40:04.634327Z","title":"Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction,","venue":null,"work_id":"049d9283-5585-4b99-b18b-df5953a09853","year":2019},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.675711Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:ececd0c4c12cc73f280de84783d4f6de62c62be7d2ae3ec23f21336a6abd8565","observation_id":"568f800f-14aa-4918-98fd-4b2d03a797b7","resolution":{"observed_at":"2026-08-12T16:40:04.639269Z","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":"2026-08-12T16:40:04.619528Z","title":"Cou- pling intent and action for pedestrian crossing behavior prediction,","venue":null,"work_id":"288cb00d-861e-4916-a150-eaa237c8448f","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.680762Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:616c78de07c38aedda55a1c68b5c157b2b151e2aa98b469b68d69dd62970766f","observation_id":"5763cc60-e779-4e33-b290-31d4a514cefa","resolution":{"observed_at":"2026-08-12T16:40:04.624792Z","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":"2026-08-12T16:40:04.605711Z","title":"The Cityscapes Dataset for Semantic Urban Scene Understanding,","venue":null,"work_id":"451d5a85-8794-499a-9218-6fd5fc4448fb","year":2016},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.685107Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:5f695d8dd11378e9526d622f6ade44fa199537071645da023c1417f21ac0e286","observation_id":"f6e05f4a-60f8-4c73-8e65-1a4233f97304","resolution":{"observed_at":"2026-08-12T16:40:04.610106Z","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":"2026-08-12T16:40:04.591809Z","title":"BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning,","venue":null,"work_id":"e0b2bc1d-2536-4c74-9c81-0ba1ab59aada","year":2020},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.689704Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:122adc032757e3d8bb70a227e5c6321326f385f45f3155a00735d15a2a965977","observation_id":"67a158c8-3c86-42f0-a035-6ae7ca028433","resolution":{"observed_at":"2026-08-12T16:40:04.596405Z","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":"2026-08-12T16:40:04.576665Z","title":"Are we ready for autonomous driving? the kitti vision benchmark suite,","venue":null,"work_id":"1941615c-4466-4082-b2ee-70a03c4a9bb4","year":2012},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.694426Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:d295a0979329d7c642f961e70785aef8c22ba781517a6a9ebf3a41ea822a86c1","observation_id":"0c3aecce-d412-4c3a-abd8-bb1415242f46","resolution":{"observed_at":"2026-08-12T16:40:04.582226Z","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":"2026-08-12T16:40:04.562633Z","title":"Vulnerable road users and connected autonomous vehicles interaction: A survey,","venue":null,"work_id":"f608403c-6f94-4a11-baae-f79ba7e6e5f6","year":2022},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.698668Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:e74ffb72c543569ad51c4c0e29f08f9a711d961ccc6e55a69e08fb0172f7e631","observation_id":"e7d93c29-bda3-47d4-8623-2df3681921ef","resolution":{"observed_at":"2026-08-12T16:40:04.567220Z","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":"2026-08-12T16:40:04.548279Z","title":"Vulnerable Road Users, Position/Policy Statement, NATIONAL SAFETY COUNCIL,","venue":null,"work_id":"d87e6544-448c-4bce-9a0d-adf1103e530f","year":2022},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.703618Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:c01fdfce9a840b4b0a38c596260a6cfa04affd0ac401f955068d70c4824c5617","observation_id":"b3c3c09f-c908-4a7a-b231-34f167fd709d","resolution":{"observed_at":"2026-08-12T16:40:04.552933Z","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":"2026-08-12T16:40:04.534408Z","title":"VULNERABLE ROAD USER (VRU) PROTECTION,","venue":null,"work_id":"3769e8ec-61da-4684-adc8-8b09aeb3aa58","year":2022},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.707956Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:312bce0148d0ce117f71a72c083286977765529d9f706fc948ce8cd91e0ff135","observation_id":"89530718-1beb-488d-8d21-32943396e354","resolution":{"observed_at":"2026-08-12T16:40:04.538862Z","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":"2026-08-12T16:40:04.520225Z","title":"Spatiotemporal relationship reasoning for pedestrian intent prediction,","venue":null,"work_id":"aeb0849f-22de-45ac-8422-a9c4c503b0fd","year":2020},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.712301Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:3d99f5ebfbb5cad91ef4e3a82e7d79471f9adef149bbc874a77c98e7bf33a519","observation_id":"a40eba53-da14-4277-ab68-7ec3e8e8fe52","resolution":{"observed_at":"2026-08-12T16:40:04.524863Z","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":"2105.08647","last_updated":"2021-05-18T16:23:15Z","snapshot_observed_at":"2026-07-06T11:10:33.637455Z","submitted_at":"2021-05-18T16:23:15Z","title":"IntFormer: Predicting pedestrian intention with the aid of the Transformer architecture","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.08647","snapshot_observed_at":"2026-08-12T16:40:03.716516Z","title":"Intformer: Predicting pedestrian intention with the aid of the transformer architecture,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.716516Z"},"links":{"cited_paper":"/paper/2105.08647","citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:a35a833cf4ef5e57ef528be540aea24479f90cc31c2147d7cbef36580f6e4d0f","observation_id":"f9dd9b16-b485-4d70-beb1-8d9c7b237ebe","resolution":{"observed_at":"2026-08-12T16:40:03.716516Z","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":"2026-08-12T16:40:04.505079Z","title":"Fussi-net: Fusion of spatio-temporal skeletons for intention prediction network,","venue":null,"work_id":"e5c25c61-303e-4370-96b5-53548bdb3459","year":2020},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.721255Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:48a56ab63eaf4dce1a0b0d096d62f4c58e7a19356005c083ff0f3096a5dab24c","observation_id":"dd0adcbd-ee5a-4aed-81a1-768f790586cd","resolution":{"observed_at":"2026-08-12T16:40:04.510210Z","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":"2026-08-12T16:40:04.490620Z","title":"Context model for pedestrian intention prediction using factored latent-dynamic condi- tional random fields,","venue":null,"work_id":"0298cb27-6cc7-4c9e-90af-f72adb5a924b","year":2020},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.725563Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:4fc9917f5f0e45e8b458dd16eab65a67e9e549130e15c03a5e49b9452316c2f5","observation_id":"9ec45aff-4c6d-4f2b-a8b5-14236fa212b2","resolution":{"observed_at":"2026-08-12T16:40:04.495361Z","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":"2026-08-12T16:40:04.476217Z","title":"Real-time intent prediction of pedestrians for autonomous ground vehicles via spatio-temporal densenet,","venue":null,"work_id":"a895df51-6f1f-47fe-b061-c8a1442e53cf","year":2019},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.729686Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:fd3d392c5bbb2694082c752e5ef459c2186e261cd3c215ddee24db49cd380dad","observation_id":"74ae767c-dad8-46c8-911f-d8f00f81e758","resolution":{"observed_at":"2026-08-12T16:40:04.480856Z","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":"2026-08-12T16:40:04.461304Z","title":"Intent prediction of vulnerable road users from motion trajec- tories using stacked lstm network,","venue":null,"work_id":"c22d08da-8c34-4614-8a1e-f865de096920","year":2017},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.733667Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:ed9aa7da9b13d8f23b320e121c9b9fcd19cb03be69e5c3d7dadca8dafab8a042","observation_id":"8365f61a-bfd2-4c39-ac66-9499c797b60c","resolution":{"observed_at":"2026-08-12T16:40:04.466155Z","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":"2026-08-12T16:40:04.445474Z","title":"Behavioral reasoning theory: Identifying new linkages underlying intentions and behavior,","venue":null,"work_id":"e4394b68-f1f0-461a-9015-55b06e047e15","year":2005},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.737788Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:5505d1a8fb150e5d2966e1bead1c8b440f8c3246dfc8405ae21e820be1458262","observation_id":"51a90498-86c8-4c07-857b-1e2893288e05","resolution":{"observed_at":"2026-08-12T16:40:04.450766Z","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":"2026-08-12T16:40:04.429780Z","title":"Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior,","venue":null,"work_id":"b7645a35-397e-4e03-9b8a-177aee77f6c8","year":2017},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.742051Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:e4ed390deeb6e38e71fbaefbbcc1a79a396ec8a89b7d4d74f2b7c99318cc33d5","observation_id":"4ae50168-bd59-4604-8b2a-583fd685b62b","resolution":{"observed_at":"2026-08-12T16:40:04.434535Z","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":"2026-08-12T16:40:04.414755Z","title":"Bifold and semantic reasoning for pedestrian behavior prediction,","venue":null,"work_id":"bbbbba37-624d-4445-b477-99f665fbe52f","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.746193Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:e9eab33f3a0dc106aa4332b197e4c27255e34aa701d633b3cfb73fd00dc4dda5","observation_id":"90a4ec05-349a-4e27-99f1-89a6d010f039","resolution":{"observed_at":"2026-08-12T16:40:04.419488Z","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":"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":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.06582","snapshot_observed_at":"2026-08-12T16:40:03.750274Z","title":"Pedestrian action antici- pation using contextual feature fusion in stacked rnns,","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.750274Z"},"links":{"cited_paper":"/paper/2005.06582","citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:73f819ba4b4775c0728915ab2ed3e0602d5b3768c304b7e2bf55104711b1a38a","observation_id":"e012cb25-675e-43f3-b0df-31c889fc1d0e","resolution":{"observed_at":"2026-08-12T16:40:03.750274Z","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":"2026-08-12T16:40:04.399600Z","title":"Graph-sim: A graph-based spatiotemporal interaction mod- elling for pedestrian action prediction,","venue":null,"work_id":"08b1d991-55fe-4c69-ba04-b2d2ca15664e","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.754757Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:69e9912a9e9dc55bc5e3e44f44cc8cb5de644725f3ac1d6c0eb4b52f0f6ef973","observation_id":"d1522eee-9dc4-4fc8-8245-f8c82b8ab1a6","resolution":{"observed_at":"2026-08-12T16:40:04.404550Z","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":"2026-08-12T16:40:04.384686Z","title":"Benchmark for evaluating pedestrian action prediction,","venue":null,"work_id":"57fbcee1-b05b-4d0b-a359-7d4a1e349ae3","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.759116Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:25f4865835bbd8002fc763428626a11b7b0af509dc1224628bfc1bbe79478557","observation_id":"0bc10c02-f63f-4a4e-b6f2-21d04127dc86","resolution":{"observed_at":"2026-08-12T16:40:04.389655Z","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":"2026-08-12T16:40:04.370425Z","title":"Social aware multi- modal pedestrian crossing behavior prediction,","venue":null,"work_id":"f6137f2a-b8fb-4bc5-91f7-e281215e7818","year":2022},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.764131Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:c84ae46ba6a741584fc3cb89ae81a8d41469bc28e058d10acdccf233ac66fa53","observation_id":"176d0f86-1ff7-4fc7-a48a-b45081c16553","resolution":{"observed_at":"2026-08-12T16:40:04.375006Z","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":"2026-08-12T16:40:03.769103Z","title":"Multi-modal hybrid architecture for pedestrian action prediction,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.769103Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:e63e6e2d6df4523f2e01d8ca2e98010da43f7af6fb377ed1bd2840ec3f6c4c30","observation_id":"79b3d094-d01e-4746-8fb6-d09c7010cd3b","resolution":{"observed_at":"2026-08-12T16:40:03.769103Z","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":"2026-08-12T16:40:04.346781Z","title":"Context-aware captions from context-agnostic supervision,","venue":null,"work_id":"64225ab0-e3f4-47b0-be2c-afbde63f6504","year":2017},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.773299Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:c94039f541f2cd7ec4f8479ddc80431a2872b09993674fbfd4c36ac846ca4584","observation_id":"c3546144-e90a-4cd4-895d-f4d49cae9d6b","resolution":{"observed_at":"2026-08-12T16:40:04.351676Z","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":"2026-08-12T16:40:04.331973Z","title":"Grounding visual explanations,","venue":null,"work_id":"10163859-8902-4cfa-bfc3-959afb2f6971","year":2018},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.778106Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:2ad8fe66b39ba07ead2ab6922b1c335964e6e400a89e49d910d9aa5946da130a","observation_id":"dbb340db-5581-4307-bc4a-47099257c92a","resolution":{"observed_at":"2026-08-12T16:40:04.336704Z","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":"2026-08-12T16:40:04.317359Z","title":"Show, attend and tell: Neural image caption generation with visual attention,","venue":null,"work_id":"dc86be59-247d-4884-a42c-5e3d01a486c3","year":2015},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.782385Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:e10c0cb9a8d0981ba865d452e20b432afe5fb71325495e65a644a6e1b799df6e","observation_id":"06854ead-d220-4a54-b6f7-183f00965aa0","resolution":{"observed_at":"2026-08-12T16:40:04.321995Z","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":"2026-08-12T16:40:03.786667Z","title":"Deep learning,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.786667Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:dc0e37c07e7bbc543ded7e700501437c0497cea1dd60e48cfbfea70ae177db74","observation_id":"73bac432-0cae-4bfe-95fd-29bd18519ba7","resolution":{"observed_at":"2026-08-12T16:40:03.786667Z","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":"2026-08-12T16:40:03.790784Z","title":"Textual explanations for self-driving vehicles,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.790784Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:731565fbb83c3154cccab2a376106771528ca3780da49a3fb46d1f07f6bee047","observation_id":"0bc3f2bc-5344-468a-a50d-73b6530e7058","resolution":{"observed_at":"2026-08-12T16:40:03.790784Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.02604","last_updated":"2026-04-23T22:37:38Z","snapshot_observed_at":"2026-07-06T12:15:30.626232Z","submitted_at":"2021-12-05T15:54:57Z","title":"PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.02604","snapshot_observed_at":"2026-08-12T16:40:03.794969Z","title":"Psi: A pedestrian behavior dataset for socially intelligent autonomous car,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.794969Z"},"links":{"cited_paper":"/paper/2112.02604","citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:b313b4de5f24a71fd36536577622a5505c9f1208b8b60c82808b80dde359bf90","observation_id":"d28c0620-92c0-427f-9d2d-dc345db748b4","resolution":{"observed_at":"2026-08-12T16:40:03.794969Z","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":"2026-08-12T16:40:04.283927Z","title":"A peek into the reasoning of neural networks: Interpreting with structural visual concepts,","venue":null,"work_id":"a1eb35e6-7d4a-4b6a-a070-48faa2825d4c","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.799875Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:dea62cc45ebb1181abffae5f1e97b773bb120e552f73dcc68ee9adcbc280c995","observation_id":"25d4b08d-5d15-4fdf-89df-51030b0eb142","resolution":{"observed_at":"2026-08-12T16:40:04.288849Z","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":"2026-08-12T16:40:04.269907Z","title":"Drive: Deep reinforced accident anticipation with visual explanation,","venue":null,"work_id":"e86e2bbd-b36a-46be-88af-fdc330c142f2","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.803958Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:810b31a9641bfe9c6e541894681d52f6fd893b30c4e3346e419a75c2fffcf767","observation_id":"dd806b61-2a96-47cc-af7b-d2e74a17fe46","resolution":{"observed_at":"2026-08-12T16:40:04.274600Z","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":"2026-08-12T16:40:04.255809Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":"4a42c806-6ef6-4780-8714-b74af4e4cd22","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.808345Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:df164c9664dfdc772a9974bdd9a7bb8bc6e771553f4ae93e8fa43f6c878b2664","observation_id":"ba0c6d4d-9b07-4d7c-abe2-3179f0852e98","resolution":{"observed_at":"2026-08-12T16:40:04.260548Z","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":"2026-08-12T16:40:04.241120Z","title":"Multimodal contrastive training for visual representation learning,","venue":null,"work_id":"3cf4cdcc-4db5-422d-8b63-e55447848c28","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.812734Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:f274e1b95b60411da3feff23ba9051992d8385b50d2c9e13885f714e0c5570ac","observation_id":"4abd2fe3-18e1-4546-8eb5-fc2cf823376a","resolution":{"observed_at":"2026-08-12T16:40:04.245922Z","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":"2026-08-12T16:40:04.226530Z","title":"Shared cross-modal trajectory prediction for autonomous driving,","venue":null,"work_id":"a86b584f-4a1f-428e-8a8a-9dc318b7b246","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.816901Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:6101a84a4bc6820780c36b14c0cc1d2bb7747094d0655e80ecb47a5ea9a88c00","observation_id":"b808f374-c887-4a54-83be-9003ac329812","resolution":{"observed_at":"2026-08-12T16:40:04.231322Z","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":"2026-08-12T16:40:04.211984Z","title":"Pedestrian inten- tion prediction for autonomous driving using a multiple stakeholder perspective model,","venue":null,"work_id":"5abe5433-41f6-4c5c-bb35-ccfd5c166d4f","year":2020},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.821146Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:07b06cb3cabad7e9289b79555950753ec44773730f6b3b88723c8d65d08df4dd","observation_id":"15fd85e8-2b30-4975-a472-22a61e8b1bb5","resolution":{"observed_at":"2026-08-12T16:40:04.216666Z","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":"2026-08-12T16:40:04.197437Z","title":"Joint intention and trajectory prediction based on transformer,","venue":null,"work_id":"c8d36bda-c6a9-494c-9418-c0bada7a0447","year":2021},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.825346Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:4e976f4af5412f8f45ce34941b93a8941cc26720de0c5d2a82a75e9f7733810e","observation_id":"88216d5f-0ceb-4fa5-85d4-b3ceb6a868c6","resolution":{"observed_at":"2026-08-12T16:40:04.202042Z","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":"1409.1556","last_updated":"2015-04-10T16:25:04Z","snapshot_observed_at":"2026-08-12T14:19:29.389332Z","submitted_at":"2014-09-04T19:48:04Z","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.1556","snapshot_observed_at":"2026-08-12T16:40:03.829524Z","title":"Very deep convolutional networks for large-scale image recognition,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.829524Z"},"links":{"cited_paper":"/paper/1409.1556","citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:7606388070b7da87787048fe8e321fde684fb77b93009daa263de107ddf380e0","observation_id":"e0e8b0f5-df33-4674-8124-51826c398201","resolution":{"observed_at":"2026-08-12T16:40:03.829524Z","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":"2026-08-12T16:40:04.182156Z","title":"Learning phrase representations using rnn encoder-decoder for statistical machine translation,","venue":null,"work_id":"71362043-ec62-49bd-af96-855537ba37cb","year":2014},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.834287Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:e6e52d5b38ffd50e0bd2c6d4949f0baefa60acc9c415cc2644d3efadbc59b7d6","observation_id":"b91b304d-7b8f-4f03-82ad-8c02bde4ffc1","resolution":{"observed_at":"2026-08-12T16:40:04.187401Z","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":"2026-08-12T16:40:04.166827Z","title":"Swin transformer v2: Scaling up capacity and resolution,","venue":null,"work_id":"ca959b56-48f5-48aa-8ccd-a5610a1d5641","year":2022},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.838450Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:ada0cf71765be7c752494f9bc93747ba4ae06019e117d98799ddb8ffa36503ce","observation_id":"98a66487-ecdc-4f68-943c-20f90c88b21d","resolution":{"observed_at":"2026-08-12T16:40:04.171750Z","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":"2026-08-12T16:40:04.152058Z","title":"Sentence-bert: Sentence embeddings using siamese bert-networks,","venue":null,"work_id":"8a5fcc76-238c-4c6f-b2fd-a5fa14c78b1d","year":2019},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.842470Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:e91c34d50df470fb9312cb364e80ebf5c6ef82cf00842af403fb37efdbc06f0b","observation_id":"518a878e-8eba-461a-897e-fb2827469745","resolution":{"observed_at":"2026-08-12T16:40:04.156832Z","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":"2026-08-12T16:40:04.138219Z","title":"Attention is all you need,","venue":null,"work_id":"b7c40559-486d-4ef6-b45d-43ce23c2c764","year":2017},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.846641Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:b22b3e50c8ed6bfccef4071ec413d8ce9a6ca88935c114ede046fdab9d4aba30","observation_id":"42dcefa3-7547-410f-83ca-73609671d087","resolution":{"observed_at":"2026-08-12T16:40:04.142526Z","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":"2026-08-12T16:40:04.124085Z","title":"Pedx: Bench- mark dataset for metric 3-d pose estimation of pedestrians in complex urban intersections,","venue":null,"work_id":"22540206-94a4-4951-9f8b-e93d9c6deffe","year":1940},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.850740Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:cc4f845a4aae3ef6091cc57c9aa8b9b112fb4f5c742b45cf70e8a69670c4951f","observation_id":"8b916601-dfe5-494d-aeb5-40b01c134e68","resolution":{"observed_at":"2026-08-12T16:40:04.128592Z","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":"2026-08-12T16:40:04.109780Z","title":"nuscenes: A multimodal dataset for autonomous driving,","venue":null,"work_id":"d422d654-638d-445b-be32-f3b9ef5695cc","year":2020},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.854681Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:a1d618282d01c7cd9fc5775cc87a6f1cf208bd3eb21456ec89a643583aa15071","observation_id":"aebda185-ae3f-4736-81f3-cadb7a499fd6","resolution":{"observed_at":"2026-08-12T16:40:04.114367Z","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":"2026-08-12T16:40:03.858818Z","title":"Intraclass correlations: uses in assessing rater reliability","venue":null,"work_id":null,"year":1979},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.858818Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:27136f3a2286f55f3d8e6f0f44b996ceac75c605234e75e458c72fc433b6bb0e","observation_id":"39df48ca-33c1-4177-aa32-124a29a055d3","resolution":{"observed_at":"2026-08-12T16:40:03.858818Z","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":"2026-08-12T16:40:04.084524Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":"aaed8e52-1140-476f-8573-1d9750e1b9e8","year":2016},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.863155Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:66d338e2289a1cc799957ceb806fa61640a622efbc2779e3af84720094f27104","observation_id":"c6733adf-19b0-4473-953b-00b6165d84f3","resolution":{"observed_at":"2026-08-12T16:40:04.089604Z","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":"2026-08-12T16:40:04.068092Z","title":"Imagenet large scale visual recognition challenge,","venue":null,"work_id":"aaf991e0-fe64-48e5-9871-719dfd5ccf71","year":2015},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.867581Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:9dab3942604760a7967998f94dde7fdc9a6f5faa397ab798515d842425faef43","observation_id":"efa28b45-4881-43da-8f93-176d882935b2","resolution":{"observed_at":"2026-08-12T16:40:04.072861Z","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":"2026-08-12T16:40:04.052029Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale,","venue":null,"work_id":"8ae4ff8d-0ba8-4dc5-9d8d-3abda158d49d","year":2020},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.871848Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:25df7a68fa5896b05db99e4abb9459555c02a6b03fd03c56757acf19f521dcfc","observation_id":"13f3b255-7bbf-4acc-8a76-aaa13f3d7bc7","resolution":{"observed_at":"2026-08-12T16:40:04.058470Z","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":"2026-08-12T16:40:04.037013Z","title":"Learning deep transformer models for machine translation,","venue":null,"work_id":"01c0b155-144b-44e7-9ac7-8a498b9dec4c","year":2019},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.875942Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:5cf0efe1cb77471f3cbd81866463adbdaf309c1375eacdd5992531878930c524","observation_id":"5412dace-c436-4833-ad98-cf17642c8ea4","resolution":{"observed_at":"2026-08-12T16:40:04.042105Z","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":"2026-08-12T16:40:04.021882Z","title":"Adaptive input representations for neural language modeling,","venue":null,"work_id":"32bcc934-9049-4f0c-9f7b-93e2902f7254","year":2018},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.880477Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:6c85043f04fa7c5c60441e59fcb2c7823b5694bb965ddf5974db0b77da6ae89e","observation_id":"874c0def-2dca-4b90-a628-7da34f793aad","resolution":{"observed_at":"2026-08-12T16:40:04.027120Z","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":"2026-08-12T16:40:04.004830Z","title":"Effective approaches to attention-based neural machine translation,","venue":null,"work_id":"990d4de1-3b16-49ae-b98e-bd37839396ff","year":2015},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.884747Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:55bc50afc1d702fffd50edbf8857d12159b8f9f350e3f4304e01ff575df75129","observation_id":"b6fc43bc-29f3-4637-916d-3a4109968276","resolution":{"observed_at":"2026-08-12T16:40:04.011714Z","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":"2010.10270","last_updated":"2021-05-20T11:14:35Z","snapshot_observed_at":"2026-08-12T05:34:33.688929Z","submitted_at":"2020-10-20T13:42:31Z","title":"Pedestrian Intention Prediction: A Multi-task Perspective","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.10270","snapshot_observed_at":"2026-08-12T16:40:03.888954Z","title":"Pedestrian intention prediction: A multi-task perspective,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.888954Z"},"links":{"cited_paper":"/paper/2010.10270","citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:7a0d58c6fb3c7bcbae63c314bbf47eeb24c32ec9b720fe454f3636a78349e545","observation_id":"414845fc-03fb-464f-aa81-25d71811f9c1","resolution":{"observed_at":"2026-08-12T16:40:03.888954Z","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":"2026-08-12T16:40:03.893730Z","title":"Glove: Global vectors for word representation,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T16:40:03.893730Z"},"links":{"citing_paper":"/paper/2411.13302"},"observation_digest":"sha256:59882ce0f0d8c88fee6c68b645ff0949558504e0559e4da24f5875680aef75c4","observation_id":"1484627d-73cf-48fd-8fcd-f97a7dd25e0d","resolution":{"observed_at":"2026-08-12T16:40:03.893730Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.13302","last_updated":"2024-11-20T13:15:04Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-13T07:02:51.830842Z","submitted_at":"2024-11-20T13:15:04Z","title":"Can Reasons Help Improve Pedestrian Intent Estimation? A Cross-Modal Approach"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":10,"verified_exact":0,"verified_fuzzy":41},"total_outbound_references":51},"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 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2411.13302."}