{"id":"W4281654129","doi":"10.5194/isprs-archives-xliii-b1-2022-257-2022","title":"AUTOMOTIVE RADAR BASED LEAN DETECTION OF VEHICLES","year":2022,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Radar; Object detection; Automotive industry; Intersection (aeronautics); Computer vision; Minimum bounding box; Pixel; Convolutional neural network; Advanced driver assistance systems; Kernel (algebra); Real-time computing; Pattern recognition (psychology); Engineering; Image (mathematics); Telecommunications; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000348616,0.0007352722,0.0004901841,0.001520477,0.0001376905,0.0004681326,0.0004820808,0.0003284236,0.0009017328],"category_scores_gemma":[0.0006292426,0.0001828717,0.0002875594,0.000594631,0.0001370268,0.0004258811,0.0006070348,0.0003328539,0.0006678575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000280393,"about_ca_system_score_gemma":0.0002847698,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00207154,"about_ca_topic_score_gemma":0.002894544,"domain_scores_codex":[0.9996438,0.00003848299,0.00001126815,0.0001097111,0.000120616,0.00007604075],"domain_scores_gemma":[0.9996585,0.00004855998,0.00004794702,0.00002579825,0.0001972908,0.00002195998],"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":[0.001026163,0.00036622,0.03852925,0.0003603715,0.0002323411,0.0005158972,0.000133884,0.07072464,0.198847,0.001069629,0.006048287,0.6821464],"study_design_scores_gemma":[0.00002487296,0.0003493023,0.03776361,0.00003864037,0.00009723447,0.0004641094,0.000117806,0.8515794,0.1055313,0.0007883271,0.00320825,0.00003714495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6760857,0.001383085,0.3119316,0.0001380803,0.0002350455,0.00008072893,0.0007061433,0.003388576,0.006051007],"genre_scores_gemma":[0.9572858,0.0002720499,0.03907131,0.00008781991,0.00002907907,0.00002010936,0.001256907,0.00003628128,0.001940599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00207154,"threshold_uncertainty_score":0.004118979,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01605219941642698,"score_gpt":0.2492237841207584,"score_spread":0.2331715847043314,"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."}}