{"id":"W1976840195","doi":"10.1109/tits.2012.2210881","title":"Pedestrian Safety Analysis in Mixed Traffic Conditions Using Video Data","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Pedestrian; Collision; SAFER; Computer science; Collision avoidance; Pedestrian crossing; Simulation; Transport engineering; Engineering; Computer security","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":[0.0004790159,0.0006140425,0.0003678132,0.003412729,0.0002910435,0.0003970984,0.0002963742,0.0003827437,0.0006316316],"category_scores_gemma":[0.0013393,0.0001482945,0.0002708531,0.001313331,0.0001795184,0.000404782,0.0003333124,0.0001835509,0.0002420507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003301546,"about_ca_system_score_gemma":0.0003102509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008813666,"about_ca_topic_score_gemma":0.01008683,"domain_scores_codex":[0.9995648,0.00006337371,0.00002823475,0.000083419,0.0001823725,0.00007768406],"domain_scores_gemma":[0.999306,0.0001503851,0.0001225623,0.00003889711,0.0003029753,0.00007927966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003875759,0.0008142033,0.4690461,0.0006005786,0.0004149856,0.002144878,0.0009179499,0.08750824,0.120923,0.0008333721,0.002379284,0.3105418],"study_design_scores_gemma":[0.00004708828,0.001520748,0.4297953,0.00007009839,0.0002285003,0.001205099,0.001655361,0.5118273,0.05066669,0.0007754836,0.002112469,0.00009586351],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9767048,0.0001488104,0.02096413,0.00002192983,0.00001705797,0.00005641843,0.0008350259,0.000190711,0.001061084],"genre_scores_gemma":[0.9883061,0.00009736557,0.01008167,0.000009007226,0.00001006292,0.0000261862,0.001121731,0.00001042109,0.0003373765],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008813666,"threshold_uncertainty_score":0.01752478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04719003252579666,"score_gpt":0.2782891438373436,"score_spread":0.2310991113115469,"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."}}