{"id":"W4388470044","doi":"10.1109/tits.2023.3324317","title":"Chance-Constrained Planning for Dynamically Stable Motion of Reconfigurable Vehicles","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"FPInnovations; McGill University","funders":"","keywords":"Motion planning; Computer science; Motion (physics); Vehicle dynamics; Control engineering; Engineering; Automotive engineering; Artificial intelligence; Robot","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005865537,0.000219745,0.0003573766,0.0004764073,0.0001946002,0.00007347354,0.0004012013,0.0001406638,0.000008923957],"category_scores_gemma":[0.000007933812,0.0002295682,0.00017459,0.0008664315,0.00004831011,0.0003703568,2.587891e-7,0.0001627814,0.00004663305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007223816,"about_ca_system_score_gemma":0.00008297619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000976373,"about_ca_topic_score_gemma":0.000007428602,"domain_scores_codex":[0.9978371,0.00006722205,0.0008179023,0.0004792067,0.0004131671,0.0003853739],"domain_scores_gemma":[0.9986298,0.0003700654,0.0002556988,0.0003762698,0.0002596657,0.0001084736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004143134,0.00009054955,0.00005835326,0.0002291543,0.00008187414,0.00000833279,0.002266151,0.9760907,0.006188706,0.001897541,0.00008406658,0.01296318],"study_design_scores_gemma":[0.0004973511,0.0002513931,0.0005573182,0.0004220217,0.00003545557,0.000006873514,0.0008391599,0.9254233,0.0711955,0.0002559394,0.0002282002,0.0002875417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01638033,0.00004158451,0.9796661,0.0001094861,0.002107699,0.0008086083,0.0002318171,0.0005280083,0.0001264401],"genre_scores_gemma":[0.9862154,0.00002788874,0.01259099,0.00001824523,0.00003527294,0.0003195005,0.00007213192,0.00002805327,0.0006924879],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9698351,"threshold_uncertainty_score":0.9361522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04906395761715301,"score_gpt":0.2839794724246136,"score_spread":0.2349155148074606,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}