{"as_of":"2026-08-21T23:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ecfd8c30df130937592e1aede43bcea2f9f1d9c352146cbb36bd0d20f8519d53","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-20T05:18:50.613788Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2605.19881/citation-record","integrity":"/paper/2605.19881/integrity","json":"/paper/2605.19881/citation-record.json","paper":"/paper/2605.19881"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-07-05T21:41:29.644470Z","title":"Autonomous vehicles on the edge: A survey on autonomous vehicle racing","venue":null,"work_id":"c0a69814-0c72-44a2-848d-47d1445254ea","year":2022},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:7481df2ec6080e3b72f5da808db666843f04729f79f41482a0512fbceef2d413","observation_id":"88d22a10-3749-4a48-a213-66464f4a4d4b","resolution":{"observed_at":"2026-05-20T05:23:21.930821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Outracing champion gran turismo drivers with deep reinforcement learning","venue":null,"work_id":"a86fc8fa-db42-4a34-acb4-58a1a3759e5b","year":2022},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:40e480428706d62d455c2f833ab6b55a5e689b1fde451fccf289abe0600be649","observation_id":"ddf7e1da-d91f-4f1b-988a-b054927076b1","resolution":{"observed_at":"2026-05-20T05:23:21.932919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Online time-optimal trajectory planning on three-dimensional race tracks","venue":null,"work_id":"e4cee833-9389-4fb0-8a69-3a467c398bce","year":2023},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:c9f6d1658455ee812c7f78c00d367f0a521d0eaa23da41026be92e1376a848b3","observation_id":"e5a96c1a-dc11-4fca-b37a-c3545864e6e4","resolution":{"observed_at":"2026-05-20T05:23:21.921579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Kineto-dynamical planning and accurate execution of minimum-time maneuvers on three-dimensional circuits","venue":null,"work_id":"93836a71-b318-420b-823c-c042505ae052","year":2025},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:786d5fe479cdcd859e6fdf80a150bd6c98922041d3f4ebe0d2504fc64366a9fa","observation_id":"b1c57526-5dad-48a8-b7a1-e81982a72ae0","resolution":{"observed_at":"2026-05-20T05:23:21.919804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Impacts of model fidelity on trajectory optimization for autonomous vehicles in extreme maneuvers","venue":null,"work_id":"7ccaa370-81d2-452c-99bd-15f6eaf3d6fa","year":2021},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:689168945191d18e7ab3c677bf958818dbb8f7698459cb9d39682237cc3b34d6","observation_id":"c6351b94-1f8a-41b4-b667-c2c5379e6b97","resolution":{"observed_at":"2026-05-20T05:23:21.930678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Time- optimal trajectory planning for a race car considering variable tyre- road friction coefficients","venue":null,"work_id":"e168db73-1b5b-4144-aaec-8453c36322f9","year":2021},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:d8123a73f5088dc65bd2bbe0800a4f32e097ab7262a44d953b98767e949f8a60","observation_id":"b84dfa48-7c0e-4fbb-96e6-b4029408bc87","resolution":{"observed_at":"2026-05-20T05:23:21.952252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"How optimal is the minimum-time manoeuvre of an artificial race driver?","venue":null,"work_id":"55604c77-d6f8-4451-abad-60b28f64bfe8","year":2025},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:26de635bb84ed25a45a0990eb45a8d8cced6140788bfeabd7897a8f327a2ca50","observation_id":"b298bc33-a6e4-4130-a2d2-69da9dba1271","resolution":{"observed_at":"2026-05-20T05:23:21.953893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Minimum curvature trajectory planning and control for an autonomous race car","venue":null,"work_id":"d6cc6358-ad5e-4aa4-9fb5-917c2a0126b5","year":2020},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:ca2e92ab0c730721857b29f73e3227a4664b7d61f06316d3f24d1ce254803ceb","observation_id":"0c06d071-1a06-4033-bf51-9f1c4a646038","resolution":{"observed_at":"2026-05-20T05:23:21.946636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Lateral control for autonomous wheeled vehicles: A technical review","venue":null,"work_id":"eba328eb-27e9-42cf-baf4-060d738efaf0","year":2023},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:122293e42b4cf89759107d78414974587fa622ad86b7ffd7641fc111d63bb4cb","observation_id":"cef5031d-e49c-497c-8800-d8d39e363a77","resolution":{"observed_at":"2026-05-20T05:23:21.910913Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Implementation of the pure pursuit path tracking algorithm","venue":null,"work_id":"7f9efc97-8ac6-4962-bea4-c38d803048c4","year":1992},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:aeb6f260d808de888b7e7657bb7d04415b5a37253a8c954ce2cb63b425b30b9a","observation_id":"feb13828-5759-43be-8385-2c2cfa34c296","resolution":{"observed_at":"2026-05-20T05:23:21.966169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"A path-tracking algorithm using predictive stanley lateral controller","venue":null,"work_id":"21a8fa21-789d-4a59-9b4b-9b1a3a87824d","year":2020},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:ec6aa2061b9d8c74b20c898e632d19c67331ed56ece32c675800d8b80b1a020f","observation_id":"5dec3b1e-a037-46d4-b5b2-0b3cc247539e","resolution":{"observed_at":"2026-05-20T05:23:21.950599Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Optimization-based hierarchical motion planning for au- tonomous racing","venue":null,"work_id":"15f5ea0d-b81e-4003-aa92-a2bd7cdf824d","year":2020},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:b6d25c48f476ce70e3bb85d6331d0330907bf00e3f5125a0b67e2989e5d8d32c","observation_id":"ea863165-1887-4fc6-a4a9-7cd5cbd0ecea","resolution":{"observed_at":"2026-05-20T05:23:21.936848Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Differentiable weights-varying nonlinear mpc via gradient- based policy learning: An autonomous vehicle guidance example","venue":null,"work_id":"09ce1139-14b5-4719-adba-29f00df1eb0e","year":2026},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:96c9522b9430e9fcf3a980cdd6186125db94eeb9a728e52ecee665889f378adb","observation_id":"2d70482a-3520-419b-b9bf-7eddf6c47d01","resolution":{"observed_at":"2026-05-20T05:23:21.899391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Rlpp: A residual method for zero-shot real-world autonomous racing on scaled platforms","venue":null,"work_id":"f85ccff2-b8e8-4215-ab87-023616188f10","year":2025},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:9c2369a33a44ad5fb799b3bce63e915c407dc1588c7501bc3e2982b53e4a2bbb","observation_id":"6d2c5051-3508-4df5-87a6-cfefa32106b8","resolution":{"observed_at":"2026-05-20T05:23:21.957298Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-07-05T21:41:29.630848Z","title":"Learning- based model predictive control for autonomous racing","venue":null,"work_id":"3306334c-77d5-4335-8964-15811da688af","year":2019},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:6555cdff7c82eb3965f527cdae7ad4922970de7878a437bc0ef71fa887b577c1","observation_id":"47628def-76b5-47f1-9c22-a4855dd189ce","resolution":{"observed_at":"2026-05-20T05:23:21.948422Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Coupled longi- tudinal and lateral control of a vehicle using deep learning","venue":null,"work_id":"bd31d5a9-0dc6-49fa-805d-604ad6ce54c7","year":2018},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:a98f626f37e244446c86cd58768982bfd771add66f9382bd927e59546274b5e1","observation_id":"f1eca0b2-f908-46d0-ad49-bff6621a5a20","resolution":{"observed_at":"2026-05-20T05:23:21.964101Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"A predictive neural hierarchical framework for on-line time-optimal motion planning and control of black-box vehicle models","venue":null,"work_id":"17090022-3360-480b-b1e6-579305b7ea16","year":2023},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:971f25ffdbb39eac3e394af4ae33d2f2c96e20a0201e4b5af7c5c519be0179a2","observation_id":"01a82325-2af9-4f74-a017-b537fd616827","resolution":{"observed_at":"2026-05-20T05:23:21.919690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Reinforcement learning and optimal control: A hybrid collision avoidance approach","venue":null,"work_id":"b694f2b0-9f38-4334-8ad8-c36ed10de2f3","year":2024},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:3ba91b6a3f5dade1581a2508daeb86cb488c4d19717636e0ab9e0f267b134b53","observation_id":"6a0e2260-c996-4cdf-ad67-268ec71dbf91","resolution":{"observed_at":"2026-05-20T05:23:21.914023Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"A mental simulation approach for learning neural-network predictive control (in self-driving cars)","venue":null,"work_id":"71e72597-ee1a-48bf-9023-7ac05b28eeae","year":2020},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:b3ad48d6911b40bfd4e4f36259dc317897384188d0376102dbbc2f3cbcd09829","observation_id":"16edf2d6-85a2-464b-928c-85b9df25a792","resolution":{"observed_at":"2026-05-20T05:23:21.917759Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"A physics-driven artificial agent for online time-optimal vehicle motion planning and control","venue":null,"work_id":"7614e923-729d-4873-9179-560431329ee6","year":2023},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:525614b1fc52116393c459a288594726265f36c01bff67b455fcb200ac4574d1","observation_id":"82ec5a27-3c50-49bc-ab46-c66ee51d2624","resolution":{"observed_at":"2026-05-20T05:23:21.942804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Model-structured neural networks to control the steering dynamics of autonomous race cars","venue":null,"work_id":"818506ae-7328-4e8e-a3a9-582394d69f1e","year":2025},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:29058904a15c31dbd1d918184806ea149c2cc03a27019bbe8c9c264f8feb564b","observation_id":"7b3d0816-14c8-4c10-80e8-b0828bbc97f2","resolution":{"observed_at":"2026-05-20T05:23:21.934785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"A road friction-aware anti-lock braking system based on model-structured neural networks","venue":null,"work_id":"ef8b43b5-5746-4b12-85f4-804f3b35cb9c","year":2025},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:416aadded3fff14a1dc4753d908c5a11e69fcb2e4bea9e9e85d9cbf40dc76f40","observation_id":"71d0b186-78d6-4d45-9b34-86aa3796e42e","resolution":{"observed_at":"2026-05-20T05:23:21.938676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Semi-analytical minimum time solutions with velocity constraints for trajectory following of vehicles","venue":null,"work_id":"6feb97bf-5645-460f-b081-5d8345ed99b4","year":2017},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:f8b21f47126702352245db83cb8b9c490af163414de2917ce23c70ac7adb02c8","observation_id":"f799341e-8cc8-457c-99bb-46953875518b","resolution":{"observed_at":"2026-05-20T05:23:21.940967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Online velocity profile generation and tracking for sampling-based local planning algo- rithms in autonomous racing environments","venue":null,"work_id":"21d36c83-5c98-4be1-8f60-6f5295e61fe6","year":2025},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:1a4f40565060fd2e949dcc0ae95002ae81d2a46f9e997f9c194cc647df8a3545","observation_id":"2526f1cf-8bcc-499a-808c-f213264728d6","resolution":{"observed_at":"2026-05-20T05:23:21.944754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Real-time velocity profile optimization for time-optimal maneuvering with generic acceleration constraints","venue":null,"work_id":"fe95cbb9-be26-42a3-95c8-51cafce2436f","year":2026},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:1e59bf30ef05d95578220d45a5cbd76dd85ff34cbb99bbc24c089dfef7c293a0","observation_id":"e13431b4-b490-4990-b86c-e5563b0f2c9f","resolution":{"observed_at":"2026-05-20T05:23:21.955631Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"On the g2 hermite interpolation problem with clothoids","venue":null,"work_id":"40cd95ec-0ee6-4f09-b0d6-973a0c3a760e","year":2018},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:464268414386ca348b47907c4169543f9a0deb66190fcdc716bbccf78d25e59b","observation_id":"da8148dd-374c-48bc-9623-4a314a36707c","resolution":{"observed_at":"2026-05-20T05:23:21.959156Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Guiggiani,The Science of V ehicle Dynamics: Handling, Braking, and Ride of Road and Race Cars","venue":null,"work_id":"7a65757d-aa45-446b-9836-80a1de99bc94","year":2018},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:5ce6f5deb7bb277d547dbfa79a02dcb0f66f2fd741e91f01bc9ee42c33911015","observation_id":"6ce5e0ab-8e0a-4431-b73e-3386f167021e","resolution":{"observed_at":"2026-05-20T05:23:21.917620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-06-05T21:23:00.469572Z","title":"Model selection and akaike’s information criterion (aic): The general theory and its analytical extensions","venue":null,"work_id":"00c02677-0a4f-4ff5-a033-5f9058a03a2b","year":1987},"citing_paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-20T05:18:50.613788Z"},"links":{"citing_paper":"/paper/2605.19881"},"observation_digest":"sha256:5d8954d13a01de59bca20410dea1f1a6038f88ae9a8e1f1aa4bdabad9603eacb","observation_id":"8c81ab0c-f3ba-4830-bce3-8d73fe716e73","resolution":{"observed_at":"2026-05-20T05:23:21.904456Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2605.19881","last_updated":"2026-05-19T14:14:07Z","latest_version":1,"primary_category":"cs.RO","snapshot_observed_at":"2026-08-21T15:34:09.397722Z","submitted_at":"2026-05-19T14:14:07Z","title":"Trajectory Planning and Control near the Limits: an Open Experimental Benchmark on the RoboRacer Platform"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":28},"total_outbound_references":28},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2605.19881."}