{"id":"W4206608377","doi":"10.1109/access.2022.3144407","title":"Autonomous Vehicles Perception (AVP) Using Deep Learning: Modeling, Assessment, and Challenges","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Benchmark (surveying); Perception; Object detection; Segmentation; Artificial intelligence; Task (project management); Component (thermodynamics); Process (computing); Field (mathematics); Deep learning; Lidar; Machine learning; Computer vision; Human–computer interaction; Systems 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001642668,0.0001118803,0.0001127912,0.00008336023,0.0006996283,0.0001700278,0.0007819113,0.00002608276,0.00001394163],"category_scores_gemma":[0.000003583907,0.0001249252,0.00002723141,0.0002439186,0.00002617557,0.0008519709,0.0006343735,0.0002851648,0.000002465844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001286719,"about_ca_system_score_gemma":0.00003314906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002409645,"about_ca_topic_score_gemma":0.00000729785,"domain_scores_codex":[0.9988721,0.0001052342,0.0001557727,0.0004415338,0.0002082312,0.0002171245],"domain_scores_gemma":[0.999431,0.00005222508,0.00009807507,0.0003197178,0.00003712938,0.00006184405],"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":[0.000001773264,0.00003457059,0.0005757246,0.000008620134,0.00000558654,0.000003561499,0.0004370049,0.8480157,0.0019941,0.003527235,0.00001083296,0.1453853],"study_design_scores_gemma":[0.0001162293,0.00003436003,0.001928419,0.000002794267,0.000005277351,0.00002691699,0.0001252716,0.9915565,0.00003399088,0.004726403,0.001297507,0.0001463822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.351642,0.0005559467,0.6464057,0.0006845549,0.0001403028,0.0001324139,6.018409e-7,0.0001906636,0.0002478057],"genre_scores_gemma":[0.9777213,0.0006308538,0.02128617,0.0001490508,0.00008364439,0.00008484724,0.00000215078,0.00001488349,0.00002708929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6260793,"threshold_uncertainty_score":0.5381045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1146892927866265,"score_gpt":0.3620709999270114,"score_spread":0.2473817071403848,"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."}}