{"as_of":"2026-08-16T23:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:661caec3b0f8bd25d88a0527f66d040cc8fa4aa814fd7041f81df07c630f6099","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-12T13:46:57.614535Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+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/2606.16589/citation-record","integrity":"/paper/2606.16589/integrity","json":"/paper/2606.16589/citation-record.json","paper":"/paper/2606.16589"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-12T13:46:57.614535Z","title":"Flow: A modular learning framework for mixed autonomy traffic,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:c2748a7263061f0d330c457717f2ef48f919ece0d3e61d115d142950744f640c","observation_id":"d0fa6d51-5a9a-4177-9463-41336f242c0f","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"SceneDiffuser: Efficient and controllable driving simulation initialization and rollout,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:965976749b918c4ecc436c2923a6ffa7cb99a872f20cd99911519d87645b520c","observation_id":"cc312bdf-a040-4b90-8dd4-7bef2eabc814","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"TrafficMCTS: A closed-loop traffic flow generation framework with group-based monte carlo tree search,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:7f8adf2900283734c6cca9a3105fd22f3b9f145a6715b3c099082847bc734c1c","observation_id":"9e4f1ab6-24a8-4150-ae88-98b5449e1120","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Transferring causal driving patterns for generalizable traffic simulation with diffusion-based distillation,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:2b2b2dc11cf9c6b968e7b60b3084fe542ffb198113ba5423b377962de1aa2c0e","observation_id":"e7474161-c2c7-4c96-8377-2b65e4822a8d","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Cooperative driving of connected autonomous vehicles in heterogeneous mixed traffic: A game theoretic approach,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:74c46cc8f810313905210c3ddfbdb2fa6dd51ddf847da9b74887aae6336f3ee0","observation_id":"5998458b-d78f-4a1e-b456-24968c94cb10","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Hybrid system stability analysis of multilane mixed-autonomy traffic,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:8d4958639c5a092c01f2bf9f2fbdcf5a83ba7b20b2936900789ed44e62bcb6d2","observation_id":"b54b6fa3-038e-4866-972a-2d2429944774","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"En- hancing safety in mixed traffic: Learning-based modeling and efficient control of autonomous and human-driven vehicles,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:274aba42f51eacccd60bfe0db3d40dbb1a5b48ff8139b45d7014792822520300","observation_id":"c0089b47-0a60-4691-bd5f-a42677ad9262","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Study on traffic flows with connected vehicles and human-driven vehicles,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:223777086cd6c7f58f9503466ae00d1fd4b914d4f8c0362b49763afcd816dd10","observation_id":"c0ed872f-b6d0-4283-b236-8dcd3f2f68c2","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Energy and environmental implications of automated vehicles under mixed autonomy traffic environment,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:a89cd26efc77ccb0f6c71d97734bd1116e716cf7deb7ae3d8ae3f4a2a23acfed","observation_id":"65ba1803-bc79-442c-84b1-2f4b1af4961a","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Analysis of roadway capacity for heterogeneous traffic flows considering the degree of trust of drivers of HVs in CA Vs,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:66b082a642110eb3c68197297223c8fe05555ec279b8b20345302d05e13b18df","observation_id":"b75653a3-e0a2-438e-98ae-70901f2f8ccc","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Exploring the impact of conditionally automated driving vehicles transferring control to human drivers on the stability of heterogeneous traffic flow,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:304e113c061fd795298e5cac4a92426a4f555d08d1750bb7e0a69ea3a57e71f7","observation_id":"ce1e1aef-a857-4dd0-9244-6ac204c0abf4","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Learning to control and coordi- nate mixed traffic through robot vehicles at complex and unsignalized intersections,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:0eddbf661ceb2b24d2ef4286285b8caded4b76223f0022414bd2be1e66de76a4","observation_id":"7c23055c-2b0b-4492-8043-0bb559bf1ffb","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Modeling and robustH inf tycontrol synthesis of the CA V-HDV heterogeneous traffic system with different car-following modes,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:a41ae5221d0446fe24387917f3c9099b640d4954d4f2e2538e2c2a9d10e40e19","observation_id":"17fb2b10-9ba8-496c-beff-28fb59fd1e25","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Urban vehicle trajectory generation based on generative adversarial imitation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:a62a9f966f7facc5a604fbab71dca5cfc3bd009f617bdcde7ecf230470fa91c9","observation_id":"91d6ffe6-b317-4a57-ab79-41c93fa5d856","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.17418","last_updated":"2025-07-23T11:21:27Z","snapshot_observed_at":"2026-08-14T15:53:39.743264Z","submitted_at":"2025-07-23T11:21:27Z","title":"Ctx2TrajGen: Traffic Context-Aware Microscale Vehicle Trajectories using Generative Adversarial Imitation Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.17418","snapshot_observed_at":"2026-07-12T13:46:57.614535Z","title":"Ctx2TrajGen: Traffic context-aware microscale vehicle trajectories us- ing generative adversarial imitation learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"cited_paper":"/paper/2507.17418","citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:29b9e1160244f812dd3fb60426b81c00833b7a6b57ae03a0d7ac354a2355dc2a","observation_id":"970ff2ba-15fc-4bfe-bada-87eae3c32c70","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"DiffAIL: Diffusion adversarial imitation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:dd338e92092bea2fff583b891945f3f13b412e2dc863b5676b9cb25f547cc47b","observation_id":"c9d08bdb-ac5c-4131-b410-4c05d8264559","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"A fast and stable framework for generative adversarial imitation learning,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:5702598bb96542dd4bda0405fd3ae2cd6fe6f9c1cce575a4cbcdf73550339c6b","observation_id":"9a6f6795-61a1-40b8-a182-b2922a50ed82","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Gen- eralizable multi-modal adversarial imitation learning for non-stationary dynamics,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:4da05835827db107cc03bb078d24456f673b9c0f4d0a962ec5161276ee6bfd0b","observation_id":"6e64bcc3-0690-41d7-94f6-c50eb2d64d1c","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"ControlTraj: Controllable trajectory generation with topology-constrained diffusion model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:5323c0e877f62c937131248093aa74cbd534739f47392dcf254eeb4d978cd25b","observation_id":"09ec19e7-b14e-42b5-9f69-f92a05654a13","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.07369","last_updated":"2024-09-11T09:06:54Z","snapshot_observed_at":"2026-08-16T14:19:28.762534Z","submitted_at":"2024-02-12T01:59:51Z","title":"Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.07369","snapshot_observed_at":"2026-07-12T13:46:57.614535Z","title":"Diff-RNTraj: A structure-aware diffusion model for road network- constrained trajectory generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"cited_paper":"/paper/2402.07369","citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:817c1d35fc766da45fef6a8428f4fc8dc6f55088fa8d6639176770dce1d86265","observation_id":"c749ede9-72b2-4a45-ad23-d74890427cd8","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Diffusion-based planning for autonomous driving with flexible guidance,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:8ea655d42b3f53163c67ce5db3bf2b569d651a4e7d50feed95e641ea83cdf73c","observation_id":"73a18378-a414-46ef-954b-6833875eea16","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Context-aware trajectory prediction for autonomous driving in heterogeneous environments,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:9a484befe3fcf007fe35a1b2d94dc198594fa7d9a36948c0d03060908dfdaeb9","observation_id":"d24d758d-9187-42aa-9e7a-6f9e7265f9ee","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Interaction- aware and driving style-aware trajectory prediction for heterogeneous vehicles in mixed traffic environment,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:cfdb17a5c0c98ff367d0deca6002d30ae3b725cc5d6ff76fc52ec0c01c73f95e","observation_id":"6aa9b831-59cf-48c5-a51b-2c36c269aced","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Post-interactive multimodal trajectory prediction for autonomous driving,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:b810fa60e748dcf82b07ab08089e54d6bad6149e0930432e4f76e65882205229","observation_id":"1fedb42d-e869-4440-93ac-fa56d163e929","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Multi-agent reinforcement learning with transformer-based spatio-temporal fusion for autonomous driving in mixed traffic,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:ecaa6fe8eb25cbcfeb66b30ae21e4acc43d88e1895fc7318fc7fb25e6886715a","observation_id":"8e7c6f38-d124-4481-bda2-4bb1749d5872","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"A planning-oriented autonomous driving framework: From image to trajectory with intent-aware prediction,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:fcb43d8935d07b0f5f9d0175b3fdec44a928004e88314ae36bcff6cfdfe966c3","observation_id":"eaa62028-c2f0-49d2-9469-1ab2eba02243","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"DragTraffic: Interactive and controllable traffic scene generation for autonomous driving,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:e2dd971d35339ae544b8d2deca3ea3cc9c5d74b6422d8918c9d85fda1c0c8e5e","observation_id":"42d47c93-f410-42ac-b0ab-bc36e62cc8d4","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"LD-Scene: LLM- guided diffusion for controllable generation of adversarial safety-critical driving scenarios,","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:16279c16e1b9cf8626bef19f61864f3486f22305a6b24a9ed49a65823b00abd6","observation_id":"315bfd54-0c97-41af-bbf6-172dc7895b6c","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Modelling two-dimensional driving behaviours at unsignalised intersection using multi-agent imitation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:3cc14cbaa5abca57905edad775bffbb59a532a3ad77bdf61455c9bd31ca7d0dd","observation_id":"4f86ae25-36fb-44c4-b5f2-dde698b0891e","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"DiffScene: Diffusion-based safety-critical scenario generation for autonomous vehicles,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:7ec86d000a19bba8845606e215ca8cbb1fcf6831c3f3b50624c2d4bb2b94544c","observation_id":"b15751b2-e58c-4413-b3a9-2a99d7437c7f","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"SceneControl: Diffusion for controllable traffic scene generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:0bc80e26fb6ba8031c68d0d962b80b296394ec23ca49713ef819a73f7e8d6618","observation_id":"7b718378-4ef1-489f-b11f-f68fd0b9bf87","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Optimizing diffu- sion models for joint trajectory prediction and controllable generation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:18ec1f44ebf50ab9cf9770e6f870527b1052e18d45726ae9db23ca0b190f6510","observation_id":"123c1632-b993-4f35-8900-8bbd2fdf3eec","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Intention-aware denoising diffusion model for trajectory prediction,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:5b551eb05f27035a7b780011e9c3971546a10f4685bdc22a50808c319d9d4159","observation_id":"baad8269-ae2f-43be-88a0-87ed4082ef70","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Traffic flow impact of mixed heterogeneous platoons on highways: An approach combining driving simulation and microscopic traffic simulation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:5aeac8585688dbfcab8f2a1d13193b2ee8c9c3893edfad15030c555b9ecad8d5","observation_id":"99a2b533-b7be-4493-a084-f7355003db3c","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"A dynamic test scenario generation method for autonomous vehicles based on conditional generative adversarial imitation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:fbb7b40880afcc3579bca14f7f0dba97112f1bb1964ddaa1766c793bc64cbe7d","observation_id":"95aa3c59-876f-4b49-bb83-3b50dc2dfbf3","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"An efficient high-risk lane-changing scenario edge cases generation method for autonomous vehicle safety testing,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:97ec1c206b1ee92c0652ec4f1103800e5bc2c8628a4aad0860b6e29206048872","observation_id":"a6a4d99e-19d1-467a-99ec-c6abc76386ee","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Detection and analysis of corner case scenarios at a signalized urban intersection,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:10cd85deda3427dfa80840d81e9594c08bc2dba290c68298dd9021287bbe9bfb","observation_id":"ebcd8609-a9ef-4bdd-ab87-e56ce4f1685c","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Application of uncertainty to out-of-distribution detection for autonomous driving perception safety,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:cc415f0d483eedf045c55635cc662b11d4fbabce75dbc881964eadd4549b93ab","observation_id":"1e96e223-1b42-41d6-91ac-48e9eff01c9e","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Optimization of conditional value- at-risk,","venue":null,"work_id":null,"year":2000},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:2f52fd82fa90ddeb31ddef6011356948c24b6b753a4dad7a6414d0b0e71a2fdf","observation_id":"3cd3f8aa-4fce-40eb-a71b-4f8e32bb47a9","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Denoising diffusion probabilistic models,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:57b8d157c02f642dbf2e7ee18820e46fa452b84f55cd0bd7a953f4366bc5eaa1","observation_id":"b042f6cd-5050-433b-9c96-5d9760770425","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Improved denoising diffusion proba- bilistic models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:7df1a343497273ba3e9535579236de29ed08c0602a8012d8b4be28b5d92d0402","observation_id":"2b901152-f170-4f5d-8455-cdaf2d1a5877","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Generative adversarial imitation learning,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:aaaa6ed9cf0c52be6400743148036c1d19e3e5aec7fa134489d5a601b69b3caf","observation_id":"30d02af0-651b-45df-a04b-e2af44ace05b","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:692bc325b95587a557fa68fd4ee21660ff287a785ffc8ed2197bd404496c666a","observation_id":"ec2ca699-fb4d-47fa-8732-ec1742965697","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:3fa99186cddc5c9cc5a4e8505fd09923a35e99a902708ac6f3719eb4b20e9bc0","observation_id":"111c9d29-f558-422c-a629-1cf763f1b36e","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"The round dataset: A drone dataset of road user trajectories at roundabouts in germany,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:3caa5d7c45ac8963e31777c2c1d88b6ec1dd7bf8e615e71055d9ef8213a2e55d","observation_id":"fd461d33-0c35-416a-8dc1-a3465596f088","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"The exid dataset: A real-world trajectory dataset of highly interactive highway scenarios in germany,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:ae0a8c89aa0605250c3b8e8b8aff1a99608557d7b7049a144c761c9e847eeace","observation_id":"8d9568f3-385c-4e3a-b946-96e029e02c25","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","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":"2026-07-12T13:46:57.614535Z","title":"The ind dataset: A drone dataset of naturalistic road user trajectories at german intersections,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-12T13:46:57.614535Z"},"links":{"citing_paper":"/paper/2606.16589"},"observation_digest":"sha256:a6d081234f1c59895f569b3200960d8eb36a9261662b7a020e02e11eac0cf997","observation_id":"f777032d-1e9e-4272-aabb-0f7f428549ce","resolution":{"observed_at":"2026-07-12T13:46:57.614535Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.16589","last_updated":"2026-07-09T07:15:09Z","latest_version":2,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-14T14:23:26.890174Z","submitted_at":"2026-06-15T11:33:24Z","title":"DRIFT: Risk-Constrained Diffusion with Imitation Priors for Mixed-Autonomy Traffic Generation"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":46,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":47},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2606.16589."}