{"id":"W4206545034","doi":"10.1109/bigdata52589.2021.9671392","title":"Drivable Area Detection Using Deep Learning Models for Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Big Data (Big Data)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Pyramid (geometry); Artificial intelligence; Segmentation; Backbone network; Computer vision; Pixel; Deep learning; Feature (linguistics); Pooling; Architecture; Image segmentation; Advanced driver assistance systems; Object detection; Pattern recognition (psychology); Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000365865,0.0002723825,0.0002826335,0.0001859572,0.0002449399,0.0002001288,0.001681598,0.0002463152,0.0001579642],"category_scores_gemma":[0.0002438156,0.00032158,0.00004982661,0.0001931403,0.00006469251,0.0009230607,0.0007220279,0.0005791914,0.00005011665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002128882,"about_ca_system_score_gemma":0.0001859551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004643182,"about_ca_topic_score_gemma":0.0008036772,"domain_scores_codex":[0.9980373,0.00004520365,0.0004165175,0.0008295024,0.0002853299,0.0003861036],"domain_scores_gemma":[0.9978955,0.000136064,0.0001164889,0.001541022,0.0002290708,0.0000818479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004766592,0.0001034368,0.0002378658,0.00005113623,0.0004143363,0.00007028727,0.00009764583,0.2120551,0.047013,0.004416725,0.0005742629,0.7349185],"study_design_scores_gemma":[0.0003885691,0.00002544057,0.00007261494,0.0000964505,0.00004164463,0.00004552808,0.000141944,0.9706408,0.01089158,0.001694302,0.01563141,0.0003296836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04400438,0.0001126372,0.9462437,0.0002490388,0.00381002,0.0001900457,0.001550647,0.0003871155,0.003452425],"genre_scores_gemma":[0.9866534,0.0003639468,0.005202461,0.00005070153,0.0006363998,0.00002463062,0.006706069,0.0000518799,0.0003104713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9426491,"threshold_uncertainty_score":0.9999236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2613181683505157,"score_gpt":0.3113148018875097,"score_spread":0.04999663353699396,"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."}}