{"as_of":"2026-08-10T17:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:61f1454e0717ec13a280ae1835552a60739fa8c9aceb963471f5027c733c7773","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T15:49:31.036962Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-10T06:31:04.303077+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/2507.14903/citation-record","integrity":"/paper/2507.14903/integrity","json":"/paper/2507.14903/citation-record.json","paper":"/paper/2507.14903"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T15:49:37.616643Z","title":"Perception, planning, control, and coordination for autonomous vehicles,","venue":null,"work_id":"2d460d62-f530-4cc2-9e25-bd57e633b721","year":2017},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:27.075788Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:1a6af31d1807aeff794462b9b264c2a567c0446ec7ef909ff8c76f4b84eb7aa6","observation_id":"3492e9c0-9358-4737-80c8-bfc1db285ab5","resolution":{"observed_at":"2026-08-06T15:49:37.751350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:37.368094Z","title":"A comprehensive review on safe reinforcement learning for au- tonomous vehicle control in dynamic environments,","venue":null,"work_id":"fb6d308c-6de5-4fb5-8d43-9d7a9f95bcb9","year":2024},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:27.154168Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:ab2a8ad9ac781aa9e7e30aa797eb5cfc9dc65dbf125b2df23f15db1328c943f4","observation_id":"3b6abe3d-6902-4636-a055-cdec7ebd8ea1","resolution":{"observed_at":"2026-08-06T15:49:37.502073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:37.167611Z","title":"Review of decision-making and planning approaches in automated driving,","venue":null,"work_id":"cd442723-384e-4294-af73-9a8719a10314","year":2022},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:27.310498Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:4844e6c77a4ccd3760c5cb882f0531d84f386d511545a00687494f661cb8d250","observation_id":"d85358ad-701f-4f30-b19e-523d954954f0","resolution":{"observed_at":"2026-08-06T15:49:37.251095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:36.724701Z","title":"Survey on artificial intelligence for vehicles,","venue":null,"work_id":"a0e26c76-c265-4bd7-9cab-d18773405f8d","year":2018},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:27.482721Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:a1b40325bf44395e4318f4a7139a3f47a11e950105bfd621bd1bf2bdc1ad1ce0","observation_id":"2cd886d2-560f-43a9-8246-d32ac2107bf5","resolution":{"observed_at":"2026-08-06T15:49:36.957101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:36.374703Z","title":"Decision-making technology for autonomous vehicles: Learning-based methods, applications and future outlook,","venue":null,"work_id":"5a5bcb75-8d9d-4ad3-89d3-9035f8b0fd35","year":2021},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:27.618121Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:89fa28e246e10ba5b2347fadd6dc2ed6d13b4dddcf0fe0fbe1ab39c38144ea6d","observation_id":"fba102ca-8cc9-4a2f-bece-de92c1ea9b7e","resolution":{"observed_at":"2026-08-06T15:49:36.567907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:36.101827Z","title":"Rule-based decision-making system for autonomous vehicles at intersections with mixed traffic environment,","venue":null,"work_id":"f768dcb0-b3c4-4aa4-af65-3f2fdc7089cc","year":2021},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:27.732518Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:7cb1b806c5767d8fba42206a817b1ad69be184b6450f280636be42a6b0927165","observation_id":"089b7c61-03e5-4f43-b01c-fd8326e4d59c","resolution":{"observed_at":"2026-08-06T15:49:36.200213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:35.875351Z","title":"Robust lane change decision for autonomous vehicles in mixed traffic: A safety-aware multi-agent adversarial reinforcement learning approach,","venue":null,"work_id":"f44d99d4-2160-409f-b899-ac73ca2bfeb0","year":2025},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:27.886088Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:a03bd2da14840745329f7b1e91115120c1d8902a8641945a1f6527d53973521b","observation_id":"f766d0a4-f8ea-47c8-87ee-d93694b1b07b","resolution":{"observed_at":"2026-08-06T15:49:35.930736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:28.051015Z","title":"An environment for autonomous driving decision- making,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:28.051015Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:bf6fc86f50d8f5a0b42618504ba67eb0c213e7213d534c5395bdcc9b8e7d60f1","observation_id":"1de7d1b2-ab7d-4a71-9451-2e7605a1ccac","resolution":{"observed_at":"2026-08-06T15:49:28.051015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1801.02805","last_updated":"2019-01-03T01:36:43Z","snapshot_observed_at":"2026-07-06T06:17:49.353933Z","submitted_at":"2018-01-09T05:56:15Z","title":"DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation","version":2},"cited_work":{"arxiv_id":"1801.02805","doi":null,"metadata_source":"pith","pith_arxiv_id":"1801.02805","snapshot_observed_at":"2026-08-06T15:49:32.326610Z","title":"DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation","venue":"cs.NE","work_id":"38947545-9fb0-4b3d-83ac-8ef1056b2bbe","year":2018},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:28.161372Z"},"links":{"cited_paper":"/paper/1801.02805","citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:99e78e83dbe9093337836c180f5c86a14e084a6ec5ad37b341eb7501f16c8a87","observation_id":"ab25397c-ccbe-4fe3-9722-97ac9fec2bc0","resolution":{"observed_at":"2026-08-06T15:49:32.445250Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:35.667097Z","title":"Investigating high-level decision making for automated driving,","venue":null,"work_id":"d6229705-ad8f-41a0-b6ba-7d5fdf6708a4","year":2023},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:28.284591Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:a1a72db6f1c57c7d1ab735201780331ac29745dd09aae07672003f1a5133af3e","observation_id":"13eab54e-2747-4b28-85bb-921c96aa060e","resolution":{"observed_at":"2026-08-06T15:49:35.774360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:35.462700Z","title":"Designing an interpretability analysis framework for deep reinforcement learning (drl) agents in highway automated driving simulation,","venue":null,"work_id":"e9000379-43d6-420e-b21f-c6540e179d78","year":2022},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:28.391236Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:060138515a7fbfd12e786bd4a573d59ec5939226eb5cdcaa6d34249228f3b008","observation_id":"7aef1229-18a1-443a-afe8-f759cfa6c35f","resolution":{"observed_at":"2026-08-06T15:49:35.527686Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2003.04371","last_updated":"2022-09-04T00:11:55Z","snapshot_observed_at":"2026-08-09T13:53:56.252787Z","submitted_at":"2020-03-09T19:15:30Z","title":"A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles","version":3},"cited_work":{"arxiv_id":"2003.04371","doi":null,"metadata_source":"pith","pith_arxiv_id":"2003.04371","snapshot_observed_at":"2026-08-06T15:49:32.115771Z","title":"A Multi-Agent Reinforcement Learning Approach For Safe and Efficient Behavior Planning Of Connected Autonomous Vehicles","venue":"cs.AI","work_id":"c5b83c15-7f1f-4e62-8fff-d8de1d2ab8f4","year":2020},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:28.542851Z"},"links":{"cited_paper":"/paper/2003.04371","citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:6519e5b442f676570637bc7d7fa108ad6f945dbd22a1246fa723dfb0ee2b181e","observation_id":"505e94fb-cfb8-45db-b43f-82935817959e","resolution":{"observed_at":"2026-08-06T15:49:32.209146Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2207.00448","last_updated":"2022-07-07T02:15:57Z","snapshot_observed_at":"2026-08-10T17:14:09.385241Z","submitted_at":"2022-07-01T14:16:50Z","title":"Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"2207.00448","doi":null,"metadata_source":"pith","pith_arxiv_id":"2207.00448","snapshot_observed_at":"2026-08-06T15:49:31.920975Z","title":"Safe Decision-making for Lane-change of Autonomous Vehicles via Human Demonstration-aided Reinforcement Learning","venue":"cs.RO","work_id":"6d7fb9b5-ccb0-47ad-b218-2db871c0bf25","year":2022},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:28.655919Z"},"links":{"cited_paper":"/paper/2207.00448","citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:8d5fe229c16b9d2c586861d73258589de2cd6f9ab79dd8f5bde2447c33af3d40","observation_id":"25cf916a-82f3-4946-a386-74920d393fc7","resolution":{"observed_at":"2026-08-06T15:49:31.986642Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:28.785132Z","title":"Hierarchical reinforcement learning: A comprehensive survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:28.785132Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:21f4da0a6c9b1256122ce952aacf6dddb6b4c8b92a4befd3e08de2f5c71aed7d","observation_id":"9ea7711e-d514-4491-a7b6-b1db65c52edf","resolution":{"observed_at":"2026-08-06T15:49:28.785132Z","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":"2026-08-06T15:49:35.257723Z","title":"Trajectory planning for autonomous vehicles using hierarchical reinforcement learning,","venue":null,"work_id":"276e5e02-3369-43fa-a622-1b63791132ba","year":null},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:28.917945Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:9623a4a732bd98117b9f7139f2256042358109b785422691c9c30f53d8eff701","observation_id":"a15c42ea-db17-4300-a326-5f48945fb23f","resolution":{"observed_at":"2026-08-06T15:49:35.381425Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:35.096238Z","title":"Action and trajectory planning for urban autonomous driving with hierarchical reinforcement learning,","venue":null,"work_id":"982402e3-97b2-47ac-a1f6-c29ac166d20c","year":null},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.164494Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:49e9ac17bf2007eed4e99329a1698bfc6927d25e298975f4c2e245c51a3c0ca4","observation_id":"a0fc35ea-d09d-4689-b575-fc41fb1a884e","resolution":{"observed_at":"2026-08-06T15:49:35.149274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.04586","last_updated":"2019-10-10T14:10:03Z","snapshot_observed_at":"2026-07-06T08:28:25.794772Z","submitted_at":"2019-10-10T14:10:03Z","title":"Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles","version":1},"cited_work":{"arxiv_id":"1910.04586","doi":null,"metadata_source":"pith","pith_arxiv_id":"1910.04586","snapshot_observed_at":"2026-08-06T15:49:31.461560Z","title":"Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles","venue":"cs.RO","work_id":"60c97d1e-1f3c-4401-88d6-822257d53efc","year":2019},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.429550Z"},"links":{"cited_paper":"/paper/1910.04586","citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:8ad4785cde0c294aa74204c1045a91e16bc216b7c4e81a25413477ab0923ec97","observation_id":"9f30a9ce-4de3-4a41-a898-2653c34a8e20","resolution":{"observed_at":"2026-08-06T15:49:31.599170Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:34.880992Z","title":"Combining decision making and trajec- tory planning for lane changing using deep reinforcement learning,","venue":null,"work_id":"153b296c-d30d-49ab-8f4c-4ce05007e6f5","year":2022},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.490344Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:34677d78d2667338b18740ebe635bc0ab19f06d2a5905b755122b6fa1c172f1e","observation_id":"8a5dd971-0d72-4ed7-8572-94ae7a01b463","resolution":{"observed_at":"2026-08-06T15:49:35.019035Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:34.653936Z","title":"Reinforcement learning,","venue":null,"work_id":"400c63fd-d638-4015-b84d-ad2288f46770","year":2025},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.589126Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:8dfa8ebf10af7345881222a76a705726282c2000e68ee9dc43c410af58830787","observation_id":"39948c68-66c5-4876-bb1d-733e2e132932","resolution":{"observed_at":"2026-08-06T15:49:34.745496Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:34.501368Z","title":"Reinforcement learning: An introduction,","venue":null,"work_id":"d127bb49-4bfb-40ae-bde5-25ab220ee794","year":1998},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.645973Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:2f4144a9fd294a0aeae6152a5aa1a83ad3850f44da20df82ca1f48bfbae89c99","observation_id":"de5f30b7-b1f9-404b-8ddb-deff20c0106d","resolution":{"observed_at":"2026-08-06T15:49:34.599742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:34.267462Z","title":null,"venue":null,"work_id":"d222327e-454a-4021-b052-6e27c2bd0c3c","year":2024},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.722142Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:e62a489894f83e60ad9271c31995de5f9a4b081f249a9e21d318ce76d92a1adf","observation_id":"63cde26c-4e26-4e09-af15-aba3fd0566ef","resolution":{"observed_at":"2026-08-06T15:49:34.323931Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:34.050995Z","title":"An efficient centralized multi-agent reinforcement learner for cooperative tasks,","venue":null,"work_id":"7e08385d-7777-4ab9-982e-c2bd351786fa","year":2023},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.812440Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:d455d1cee6674a72fa3c7f07a08e797467f97c7fa4e9cee48a0930948098d838","observation_id":"7c2047de-6660-42c2-95ca-92fddd5b2c0f","resolution":{"observed_at":"2026-08-06T15:49:34.154968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1710.06117","last_updated":"2017-10-18T04:54:31Z","snapshot_observed_at":"2026-07-06T06:04:35.660119Z","submitted_at":"2017-10-17T06:26:44Z","title":"Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning","version":2},"cited_work":{"arxiv_id":"1710.06117","doi":null,"metadata_source":"pith","pith_arxiv_id":"1710.06117","snapshot_observed_at":"2026-08-06T15:49:31.325298Z","title":"Map-based Multi-Policy Reinforcement Learning: Enhancing Adaptability of Robots by Deep Reinforcement Learning","venue":"cs.RO","work_id":"306b0db4-adac-4128-93bc-8902a7977ded","year":2017},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.892817Z"},"links":{"cited_paper":"/paper/1710.06117","citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:122f191497b158158d2c6ec429f1bb656ac3a2ed1aedf6e415f4c36de43ae23a","observation_id":"84092647-64b7-436b-9877-aa5646af6a4e","resolution":{"observed_at":"2026-08-06T15:49:31.373279Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:33.902245Z","title":"Multi-policy deep reinforcement learning for multi-objective multiplicity flexible job shop scheduling,","venue":null,"work_id":"2cf34297-2463-4011-bb7e-6de11e01a3fb","year":2024},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.997710Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:adc029385e4523a70ec70bbc4d14938dff0b8d4c03c035b9499cf402f9fdb0a1","observation_id":"f166d797-1e8a-445b-8c10-35bf3ba105f3","resolution":{"observed_at":"2026-08-06T15:49:33.943153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:30.161549Z","title":"Long short-term memory,","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:30.161549Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:1a38a76c58d866874e34edd17d2b1a217fd2f6728b7bc94c31c5d9e7401721fa","observation_id":"66f45b37-7a2b-42b0-a694-a2ac1abc4345","resolution":{"observed_at":"2026-08-06T15:49:30.161549Z","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":"2026-08-06T15:49:30.276019Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:30.276019Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:95ccbfb183067281bb1484d44128c3dcb0727c40c1878a57da40be3a07be99c9","observation_id":"6cc9f661-dc4a-47c1-8493-027fcd831669","resolution":{"observed_at":"2026-08-06T15:49:30.276019Z","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":"2026-08-06T15:49:33.614430Z","title":"Educational applications of the cyber-physical mobility lab: A summary,","venue":null,"work_id":"415c8b35-3e81-486f-8b6b-d3910db9c39e","year":2024},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:30.544374Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:638c4766c5c33c097424d60d3afc5f816ce57bf96d5f45f49a47aab65beb1806","observation_id":"39b08bb4-af4c-4609-89fd-c34ac325a96c","resolution":{"observed_at":"2026-08-06T15:49:33.773427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:33.329287Z","title":"Sigmarl: A sample-efficient and gen- eralizable multi-agent reinforcement learning framework for motion planning,","venue":null,"work_id":"4204c01c-6803-4511-950e-2f374c57d601","year":2024},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:30.676239Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:08f1161f92555405faf4fdc2cd2fb063f3b406b7c58c917740557ef10e2cf5ae","observation_id":"4d597e6f-1e8e-41ce-ba2a-c8d45487c0b9","resolution":{"observed_at":"2026-08-06T15:49:33.463403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.03762","last_updated":"2023-08-02T00:41:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2017-06-12T17:57:34Z","title":"Attention Is All You Need","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.03762","snapshot_observed_at":"2026-08-06T15:49:30.423762Z","title":"Available: https://arxiv.org/abs/1706.03762","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:30.423762Z"},"links":{"cited_paper":"/paper/1706.03762","citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:cbb78ab1d6b0be50589d6aad31950f5859b6e9aaabe05a6637cf1cc29d167035","observation_id":"ec40af7a-6b14-4ef2-93f9-b2a7574937bd","resolution":{"observed_at":"2026-08-06T15:49:30.423762Z","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":"2026-08-06T15:49:32.836602Z","title":"Learning-based control barrier function with provably safe guarantees: Reducing conservatism with heading-aware safety margin,","venue":null,"work_id":"5ebab910-3263-4416-965b-a1bf287b59c9","year":2025},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:30.894151Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:228202f36e7f5df5937af3729186fad2c40a61a116dff208d1dac762b734340e","observation_id":"00dfa195-4bfe-4463-8406-e7dcd78981ba","resolution":{"observed_at":"2026-08-06T15:49:32.949004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:32.567616Z","title":"A real-time control barrier function- based safety filter for motion planning with arbitrary road boundary constraints,","venue":null,"work_id":"918e5de5-23c0-4c4a-803c-0e194fe4d464","year":2025},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:30.989487Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:180f6b4e81686048dd5c94cbd82ce434a443e3b81ccf3542f287fd93223e1224","observation_id":"eea7015c-8afb-42d1-8fb3-5047b987c073","resolution":{"observed_at":"2026-08-06T15:49:32.729390Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+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":"2026-08-06T15:49:33.070669Z","title":"Lanelets: Efficient map repre- sentation for autonomous driving,","venue":null,"work_id":"59decfdc-ea64-478e-990a-dc617ea6efda","year":2014},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:30.800508Z"},"links":{"citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:4f332e95fac2067805d9f93f284ac3ff5d14fe514a0353d8083538137666b4f5","observation_id":"5b1664db-6ed7-4982-9043-3011620b7e0a","resolution":{"observed_at":"2026-08-06T15:49:33.204479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.15014","last_updated":"2025-03-19T09:11:56Z","snapshot_observed_at":"2026-08-07T16:52:43.421284Z","submitted_at":"2025-03-19T09:11:56Z","title":"High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation","version":1},"cited_work":{"arxiv_id":"2503.15014","doi":null,"metadata_source":"pith","pith_arxiv_id":"2503.15014","snapshot_observed_at":"2026-08-06T15:49:31.135345Z","title":"High-Order Control Barrier Functions: Insights and a Truncated Taylor-Based Formulation","venue":"eess.SY","work_id":"792f9fbf-9ecb-4679-8c08-aa9323e3d878","year":2025},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:31.036962Z"},"links":{"cited_paper":"/paper/2503.15014","citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:d6d2db35e5f8b9066ba23b88652230d3f8a45468f3aef04043ff80f1b15d4006","observation_id":"3f6b82be-ee79-495c-a5a7-28340e90c581","resolution":{"observed_at":"2026-08-06T15:49:31.214934Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2011.04752","last_updated":"2020-11-09T20:49:54Z","snapshot_observed_at":"2026-08-10T11:15:19.381396Z","submitted_at":"2020-11-09T20:49:54Z","title":"Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.04752","snapshot_observed_at":"2026-08-06T15:49:29.017955Z","title":"Available: https://arxiv.org/abs/2011.04752","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.017955Z"},"links":{"cited_paper":"/paper/2011.04752","citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:cdc5b20ce66e8cb2757050832b4c4fbbbe7d87d60a1bc296c7fea7039a7ed605","observation_id":"e314a9f8-bf40-473b-8dfb-9afd5b5f57e2","resolution":{"observed_at":"2026-08-06T15:49:29.017955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.15968","last_updated":"2023-06-28T07:11:02Z","snapshot_observed_at":"2026-07-06T15:47:38.256580Z","submitted_at":"2023-06-28T07:11:02Z","title":"Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2306.15968","doi":null,"metadata_source":"pith","pith_arxiv_id":"2306.15968","snapshot_observed_at":"2026-08-06T15:49:31.760485Z","title":"Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning","venue":"cs.RO","work_id":"307bbda6-ffd4-420c-a11f-220502b50378","year":2023},"citing_paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T15:49:29.279067Z"},"links":{"cited_paper":"/paper/2306.15968","citing_paper":"/paper/2507.14903"},"observation_digest":"sha256:bc2917aeeda09073fbe2e4fa0c3ea570399f01bb4f0d507c587947b35a8d3343","observation_id":"8d8d47ab-ce7d-4e2f-af24-39a4d6fa910a","resolution":{"observed_at":"2026-08-06T15:49:31.840469Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.14903","last_updated":"2025-07-20T10:27:44Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-07T15:32:04.643888Z","submitted_at":"2025-07-20T10:27:44Z","title":"CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":7,"verified_exact":6,"verified_fuzzy":21},"total_outbound_references":35},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.14903."}