{"id":"W4393864821","doi":"10.1109/jiot.2024.3362851","title":"Overtaking Mechanisms Based on Augmented Intelligence for Autonomous Driving: Data Sets, Methods, and Challenges","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Overtaking; Computer science; Artificial intelligence; Rendering (computer graphics); Object detection; Segmentation; Obstacle; Automation; Field (mathematics); Context (archaeology); Human–computer interaction; Computer vision; 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":[0.001915295,0.001849553,0.001272754,0.002799628,0.0007792992,0.00357488,0.003000031,0.002412332,0.001338189],"category_scores_gemma":[0.00650174,0.0007545153,0.001660995,0.001908479,0.001331055,0.002814507,0.001961388,0.002619803,0.0013381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001072341,"about_ca_system_score_gemma":0.001299606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01922009,"about_ca_topic_score_gemma":0.01949005,"domain_scores_codex":[0.997907,0.0004506547,0.0001701849,0.0005493347,0.0007431776,0.0001796674],"domain_scores_gemma":[0.9969317,0.001016014,0.0001814596,0.0009198241,0.0008018852,0.0001491536],"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":[0.0007282099,0.0008874928,0.04325892,0.002481078,0.0005543453,0.0004803873,0.0006475227,0.2448924,0.01079549,0.009441318,0.02979725,0.6560356],"study_design_scores_gemma":[0.00004046204,0.0002845844,0.02190107,0.0005611489,0.0001443545,0.0005357118,0.001063238,0.9137066,0.01206046,0.01863267,0.03090322,0.0001664368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4254411,0.05332855,0.4645644,0.005850411,0.001494655,0.0008567381,0.0180991,0.01282865,0.01753649],"genre_scores_gemma":[0.7732809,0.008627184,0.1755544,0.0004907288,0.0002688013,0.0004449173,0.03702499,0.0003509811,0.003957046],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01922009,"threshold_uncertainty_score":0.03821641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1091354130212905,"score_gpt":0.382549577281926,"score_spread":0.2734141642606355,"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."}}