{"id":"W4226198919","doi":"10.1109/tgcn.2022.3162698","title":"Green-Tech CAV: Next Generation Computing for Traffic Sign and Obstacle Detection in Connected and Autonomous Vehicles","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Obstacle; Computer science; Traffic sign recognition; Real-time computing; Traffic sign; Intelligent transportation system; Advanced driver assistance systems; Sign (mathematics); Artificial intelligence; Transport 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002374358,0.00033841,0.0002342399,0.0004949532,0.0002903493,0.0007055686,0.0007957113,0.0004891071,0.004464877],"category_scores_gemma":[0.0004707304,0.0001173066,0.0001947581,0.0005704726,0.0003194532,0.0008146965,0.0006055699,0.0005485088,0.001210869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000469481,"about_ca_system_score_gemma":0.0006203637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00465617,"about_ca_topic_score_gemma":0.004424697,"domain_scores_codex":[0.9998361,0.0000217222,0.000006136459,0.00002943616,0.00007904738,0.0000275931],"domain_scores_gemma":[0.9998724,0.00002131837,0.000007532715,0.00002294952,0.00005932471,0.00001640458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004081222,0.0002660762,0.003452353,0.0003900321,0.00007774335,0.0003636525,0.0002514339,0.05250455,0.03215074,0.03952052,0.0766703,0.7939445],"study_design_scores_gemma":[0.00005871115,0.0003109592,0.00223012,0.0001054762,0.00003486278,0.0002404305,0.0001480053,0.8217205,0.02407704,0.0212762,0.129752,0.00004565461],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04850613,0.008097562,0.8732801,0.001680362,0.001261566,0.0002919585,0.0005855211,0.01706798,0.0492288],"genre_scores_gemma":[0.7372635,0.003843227,0.2234701,0.0008506365,0.0002191837,0.0002877702,0.001587032,0.0005945446,0.03188401],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00465617,"threshold_uncertainty_score":0.01493645,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03874289677801138,"score_gpt":0.2335730253435476,"score_spread":0.1948301285655362,"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."}}