{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001263714,0.0001777088,0.0002163197,0.0001937491,0.00009662682,0.0003020616,0.001758593,0.00006229787,0.000007947705],"category_scores_gemma":[0.0001446741,0.000153774,0.00007384612,0.000154127,0.00004484482,0.0009202234,0.0003404415,0.0003822806,0.000003197332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006734099,"about_ca_system_score_gemma":0.00006157921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004332787,"about_ca_topic_score_gemma":0.000002099892,"domain_scores_codex":[0.9984418,0.00009138109,0.0004293351,0.0005427937,0.0002398032,0.0002548888],"domain_scores_gemma":[0.9979296,0.00100983,0.0002459849,0.0006262671,0.00007695974,0.0001113354],"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":[0.00002091688,0.00005828124,0.00000171275,0.0001124474,0.00007426571,0.00002118726,0.001046283,0.002305945,0.00670389,0.1135799,0.001057494,0.8750176],"study_design_scores_gemma":[0.00008232312,0.0001955097,0.000006149535,0.0004614668,0.0000178632,0.0001452626,0.00002862729,0.8953094,0.02781327,0.06842528,0.007377259,0.0001375959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002276819,0.001736469,0.9927258,0.003786221,0.001076677,0.0001898884,0.00000462645,0.0001242307,0.000128424],"genre_scores_gemma":[0.2540033,0.000452628,0.7450004,0.0003666218,0.0000805899,0.00001321293,0.000002383717,0.00002132236,0.00005945148],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8930035,"threshold_uncertainty_score":0.6270721,"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."}}