{"id":"W4389579861","doi":"10.1155/2023/5583901","title":"Automated Lane-Level Road Geometry Estimation Using Microscopic Trajectory Data","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Calibration; Tracking (education); Process (computing); Radar; Lidar; Cluster analysis; Computer vision; Artificial intelligence; Remote sensing; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000162133,0.0005704649,0.0003818315,0.00168667,0.0002002518,0.0004504807,0.0005042343,0.0003290288,0.0008519581],"category_scores_gemma":[0.0008265947,0.0002439824,0.0003171542,0.001114484,0.0001762292,0.0008539443,0.0005510463,0.0003666932,0.0007268382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003819065,"about_ca_system_score_gemma":0.0005494352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01016486,"about_ca_topic_score_gemma":0.01164695,"domain_scores_codex":[0.9997531,0.00002857974,0.000008346085,0.00008026769,0.00009539408,0.00003441761],"domain_scores_gemma":[0.9995399,0.00005564783,0.0001066155,0.00009326059,0.000179832,0.00002482685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002219767,0.0001575989,0.04036216,0.0001572149,0.0001075855,0.0002242363,0.0002432776,0.3504423,0.08776861,0.001523138,0.002255281,0.5165365],"study_design_scores_gemma":[0.000008905848,0.00008690137,0.02239659,0.00001086371,0.00001810343,0.0001005263,0.0001286119,0.9555598,0.019071,0.0007542576,0.001829956,0.0000344284],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2887649,0.0001795848,0.7054092,0.00004670381,0.00002805147,0.00007230283,0.0006968601,0.003330334,0.001472093],"genre_scores_gemma":[0.8782489,0.0001565398,0.1189076,0.000009635345,0.00001162463,0.00003672244,0.001508711,0.00009468425,0.001025589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01016486,"threshold_uncertainty_score":0.0202114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03135638838417788,"score_gpt":0.28922120953243,"score_spread":0.2578648211482522,"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."}}