{"id":"W4226061075","doi":"10.3390/vehicles4020021","title":"Motion Planning for Autonomous Vehicles Based on Sequential Optimization","year":2022,"lang":"en","type":"article","venue":"Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Kinematics; Acceleration; Trajectory; Trajectory optimization; Control theory (sociology); Nonlinear system; Computer science; Finite element method; Node (physics); Mathematics; Engineering; Physics; Artificial intelligence; Classical mechanics; Structural engineering","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":[0.0005885922,0.000784457,0.0007436777,0.0005725654,0.0004344135,0.0004690527,0.0006032533,0.0004075991,0.001686824],"category_scores_gemma":[0.0008678929,0.0004943939,0.0006737268,0.0004442351,0.0007136036,0.0006245613,0.0007414911,0.0006052713,0.000258577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007137025,"about_ca_system_score_gemma":0.001186732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008502873,"about_ca_topic_score_gemma":0.006160351,"domain_scores_codex":[0.9996688,0.00007088739,0.00001673494,0.0000747473,0.0001284746,0.00004038676],"domain_scores_gemma":[0.9996464,0.0001823216,0.00004972777,0.00002987386,0.00007222092,0.00001950187],"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":[0.00002915332,0.00001369876,0.0002090092,0.00003898166,0.00001385213,0.00002060959,0.00004441278,0.9658036,0.002018815,0.009139152,0.0001896818,0.02247904],"study_design_scores_gemma":[0.000004567408,0.00002751024,0.00004825909,0.000003422848,0.000002735159,0.0000081362,0.000005935894,0.9963043,0.0003815687,0.002683023,0.0005279345,0.000002642226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007133096,0.00007231168,0.9911711,0.00002609056,0.000009366366,0.00002305199,0.0000145138,0.0001037236,0.001446667],"genre_scores_gemma":[0.5853793,0.0002856157,0.409661,0.00004166889,0.00002897469,0.0003024512,0.0001425553,0.0001240811,0.004034412],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008502873,"threshold_uncertainty_score":0.01690674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349628547379694,"score_gpt":0.2689507922467902,"score_spread":0.2339879375088208,"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."}}