{"id":"W3129804215","doi":"10.1049/iet-its.2020.0087","title":"Predicting driver behaviour at intersections based on driver gaze and traffic light recognition","year":2020,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Color perception and design","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gaze; Computer science; Computer vision; Artificial intelligence; Advanced driver assistance systems","routes":{"ca_aff":true,"ca_fund":true,"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","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0001713447,0.0002680256,0.0003054249,0.0001592923,0.0001736701,0.00003248407,0.0001204767,0.0002252191,0.004323367],"category_scores_gemma":[0.00001046387,0.0002643726,0.0001838847,0.0002189895,0.00006026469,0.00007557945,0.000007619929,0.0003018384,0.001396431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001139548,"about_ca_system_score_gemma":0.00001937762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001309477,"about_ca_topic_score_gemma":0.0002497036,"domain_scores_codex":[0.9981183,0.000152358,0.0005503499,0.0006083039,0.0002685365,0.0003020947],"domain_scores_gemma":[0.9991793,0.00007813394,0.0001194045,0.0002299769,0.00009764457,0.0002955624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004744751,0.002481329,0.7321822,0.0006953941,0.0007226942,0.0009575609,0.1600328,0.01319036,0.009969945,0.0003806786,0.05630956,0.01833267],"study_design_scores_gemma":[0.01245203,0.0122954,0.4771886,0.0029958,0.00234193,0.0007390271,0.07782286,0.148586,0.00667423,0.00002973389,0.2533363,0.00553813],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97983,0.00007850947,0.009443494,0.0005962409,0.002037818,0.0008491742,0.0001333733,0.0004061036,0.006625236],"genre_scores_gemma":[0.9971781,0.00002121661,0.00002420062,0.0007437801,0.0003074672,0.0001513654,0.0001566854,0.00004734403,0.001369884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2549936,"threshold_uncertainty_score":0.9999809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06012571686173832,"score_gpt":0.2801514026356062,"score_spread":0.2200256857738679,"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."}}