{"id":"W4293095155","doi":"10.1109/vtc2022-spring54318.2022.9860676","title":"Object Detection for Connected and Autonomous Vehicles using CNN with Attention Mechanism","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Object detection; Convolutional neural network; False positive paradox; Computer vision; Deep learning; Inference; Pattern recognition (psychology)","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","sts"],"consensus_categories":[],"category_scores_codex":[0.000371127,0.0003822381,0.0004235045,0.0005895224,0.001501052,0.0001296048,0.0010245,0.0002284888,0.00001666453],"category_scores_gemma":[0.00004030878,0.0004093251,0.0000971166,0.001608051,0.000221056,0.0003525033,0.0007544544,0.0008057683,0.000006369906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002996344,"about_ca_system_score_gemma":0.000183254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007835775,"about_ca_topic_score_gemma":0.0001160206,"domain_scores_codex":[0.9971667,0.0001122721,0.0004290016,0.00123213,0.0003990962,0.0006608154],"domain_scores_gemma":[0.9981802,0.0001082761,0.000386505,0.0009886824,0.0002303755,0.0001059354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000695955,0.0001586907,0.0009001556,0.00004854234,0.0001559741,0.0000621719,0.0001418251,0.006457936,0.6698599,0.2919558,0.00001625438,0.03017317],"study_design_scores_gemma":[0.001991986,0.001119096,0.001927527,0.00004080153,0.0001367686,0.0004640526,0.0003988927,0.8425435,0.08122268,0.06600909,0.003138586,0.001007028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4856769,0.0001607636,0.5117969,0.0005588045,0.0002577267,0.0008048631,0.00001046262,0.0007214679,0.00001218687],"genre_scores_gemma":[0.9546324,0.00003231502,0.04364463,0.0001604493,0.00004837533,0.001358205,0.00000724968,0.00005106156,0.00006532647],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8360856,"threshold_uncertainty_score":0.9998358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01769779640582466,"score_gpt":0.2412152179047322,"score_spread":0.2235174214989075,"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."}}