{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000244339,0.0005038259,0.0004552519,0.0003427856,0.0003673098,0.0003853126,0.000578002,0.0003401343,0.001608551],"category_scores_gemma":[0.0008158607,0.0003838629,0.0004912119,0.0003254051,0.000543386,0.0004521572,0.0006101275,0.0004661914,0.0001631948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005928354,"about_ca_system_score_gemma":0.0009476498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007519978,"about_ca_topic_score_gemma":0.007244778,"domain_scores_codex":[0.9998272,0.00003382157,0.000007526602,0.00003913647,0.00006305661,0.00002933297],"domain_scores_gemma":[0.9997349,0.0001479169,0.00004472267,0.0000210506,0.00003110015,0.00002032425],"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":[0.00002397999,0.00000405864,0.0001613254,0.00001773402,0.00000725945,0.00003955537,0.00002285927,0.9776643,0.001027675,0.006566848,0.0002152325,0.01424922],"study_design_scores_gemma":[0.000002891908,0.000008967503,0.00004526458,0.000002079021,0.000001264244,0.000008491508,0.000003567758,0.9969009,0.0002994617,0.002419631,0.0003047021,0.000002789823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01283321,0.00008277716,0.9852577,0.00004665794,0.00001436417,0.00001825861,0.00002778288,0.0001521358,0.001567007],"genre_scores_gemma":[0.780032,0.0001496572,0.2171669,0.00004321138,0.00002567037,0.000123001,0.0001114216,0.00008085592,0.002267308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007519978,"threshold_uncertainty_score":0.01495242,"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."}}