{"id":"W3173280621","doi":"10.1109/cvpr46437.2021.00095","title":"SceneGen: Learning to Generate Realistic Traffic Scenes","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":88,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Heuristics; Computer science; Fidelity; Artificial intelligence; Set (abstract data type); Autoregressive model; Perception; Scale (ratio); Machine learning; Computer vision","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":[],"consensus_categories":[],"category_scores_codex":[0.00007021886,0.0001054857,0.0001301306,0.00005241855,0.00008780802,0.00001784334,0.00008966197,0.0001080879,0.0002999903],"category_scores_gemma":[0.00003181683,0.0001102372,0.00003338879,0.0002379482,0.00001678822,0.0000335477,0.00003416191,0.0001859347,0.0003437033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003607506,"about_ca_system_score_gemma":0.00002328633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003416209,"about_ca_topic_score_gemma":0.00005052463,"domain_scores_codex":[0.9993877,0.00001800091,0.000137088,0.0001657892,0.00005492179,0.0002364676],"domain_scores_gemma":[0.9996939,0.00002032962,0.000007083602,0.0001782879,0.00003144353,0.00006895536],"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":[0.000002896131,0.00001200016,0.0002696948,0.00002128966,0.00003239464,0.00007781334,0.0001559509,0.9134431,0.01460872,0.003524042,0.001601941,0.06625015],"study_design_scores_gemma":[0.0005213239,0.00008085202,0.005603078,0.00004621935,0.00004229883,0.0001408094,0.0006080694,0.7019793,0.2128822,0.0003413314,0.07684357,0.0009108938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9678076,0.0005210227,0.01316108,0.0006240353,0.0002281383,0.00006230624,0.00000234113,0.002200287,0.0153932],"genre_scores_gemma":[0.9929676,0.00008262653,0.003953463,0.0001542612,0.00005737421,0.000008852396,0.00001078276,0.00002582744,0.002739209],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2114638,"threshold_uncertainty_score":0.4495341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007642319670971317,"score_gpt":0.201149160636231,"score_spread":0.1935068409652597,"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."}}