{"id":"W4387869981","doi":"10.1109/mlsp55844.2023.10285959","title":"Federated Cooperative 3D Object Detection for Autonomous Driving","year":2023,"lang":"en","type":"article","venue":"","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Benchmark (surveying); Intersection (aeronautics); Federated learning; Set (abstract data type); Focus (optics); Object (grammar); Data mining; Distributed computing; Artificial intelligence; Machine learning; Transport engineering; Engineering","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":[0.001434403,0.0009884924,0.001269383,0.001323772,0.0007627375,0.001197167,0.002611044,0.0012908,0.0008208624],"category_scores_gemma":[0.002903746,0.0005199163,0.0008957867,0.001023297,0.0008311127,0.001887595,0.003031255,0.00115518,0.0004837435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001056939,"about_ca_system_score_gemma":0.001239129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009448742,"about_ca_topic_score_gemma":0.009860227,"domain_scores_codex":[0.9989522,0.0001946437,0.0000373395,0.0003571211,0.0003211549,0.0001375405],"domain_scores_gemma":[0.9989001,0.0002391741,0.0001049978,0.0003993295,0.0002765288,0.00007987236],"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":[0.000337696,0.0004017359,0.008658385,0.00008457308,0.0001684002,0.0002063314,0.0003322095,0.517242,0.01202489,0.004094769,0.003962924,0.4524863],"study_design_scores_gemma":[0.000004451055,0.00002071879,0.0004829907,0.000003108277,0.000005496215,0.00003238609,0.00003693443,0.9935619,0.002609021,0.002786976,0.0004497907,0.000006297176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09658878,0.0002740495,0.896055,0.0002262091,0.0000538123,0.00006829866,0.0001849781,0.004848646,0.00170021],"genre_scores_gemma":[0.8728725,0.00007779911,0.1249648,0.0001250611,0.00001635546,0.00005966311,0.0004700815,0.00008079218,0.001333007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009448742,"threshold_uncertainty_score":0.0187875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03359074256263292,"score_gpt":0.2865782889840489,"score_spread":0.252987546421416,"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."}}