{"id":"W3091927272","doi":"10.1109/iemtronics51293.2020.9216400","title":"Autonomous Mobility Vehicle","year":2020,"lang":"en","type":"article","venue":"2020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Interfacing; Computer science; Installation; Image processing; Embedded system; Object detection; IVMS; Automotive engineering; Real-time computing; Artificial intelligence; Engineering; Computer hardware; Vehicle tracking system; Image (mathematics); Kalman filter","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002691323,0.0004397646,0.0004515689,0.0000824558,0.0001653058,0.000121162,0.0007939462,0.0003782915,0.0003488103],"category_scores_gemma":[0.00004451554,0.0004885047,0.0001542301,0.000212709,0.0001411368,0.0002390569,0.0001653299,0.001207312,0.0001771864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004857789,"about_ca_system_score_gemma":0.0004182848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001425245,"about_ca_topic_score_gemma":0.00007655028,"domain_scores_codex":[0.9975289,0.00003877963,0.0005640678,0.0006888621,0.0003261831,0.0008532797],"domain_scores_gemma":[0.9989983,0.00006768248,0.0001142592,0.0003782664,0.00015332,0.0002881212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002418509,0.0002540543,0.0007257895,0.000178933,0.001108688,0.00005045056,0.001149629,0.03853202,0.06727398,0.7862682,0.003871596,0.1003448],"study_design_scores_gemma":[0.001029415,0.0003192681,0.0001540648,0.00001952525,0.00005525292,0.00002387974,0.0001332833,0.8473607,0.01475619,0.01197794,0.1235148,0.0006556959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9055815,0.008274615,0.06041494,0.01366411,0.001443947,0.0008253913,0.000199966,0.002315977,0.007279529],"genre_scores_gemma":[0.9939801,0.004092345,0.0007998351,0.000549407,0.0002258219,0.00005930272,0.00005414538,0.00006864542,0.0001704773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8088287,"threshold_uncertainty_score":0.9997566,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01462546772435514,"score_gpt":0.2245360565727387,"score_spread":0.2099105888483836,"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."}}