{"id":"W3194037986","doi":"10.1109/tits.2021.3102479","title":"LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Korea Creative Content Agency; National Research Foundation of Korea; Ministry of Culture, Sports and Tourism","keywords":"Artificial intelligence; Computer science; Computer vision; RGB color model; Frame (networking); Feature extraction; Advanced driver assistance systems; Encoder; Object detection; Segmentation","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"],"consensus_categories":[],"category_scores_codex":[0.0002429672,0.0002977905,0.0003331159,0.000377003,0.0002392675,0.0000514734,0.0001201219,0.000319687,0.00009368973],"category_scores_gemma":[0.000001905247,0.000337108,0.0001660506,0.0006588159,0.00003888779,0.0001601373,3.358995e-7,0.0004583062,0.000086368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003100791,"about_ca_system_score_gemma":0.00003795707,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000992989,"about_ca_topic_score_gemma":0.0003922318,"domain_scores_codex":[0.9981905,0.00008628463,0.0006475875,0.0004420326,0.0002917346,0.0003418852],"domain_scores_gemma":[0.9992855,0.00007223267,0.00006513655,0.0003508547,0.000104853,0.0001214792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000042976,0.00009466949,0.00002405219,0.0001737567,0.0001348692,0.00002786841,0.0007301208,0.9011976,0.06316475,0.00006102071,0.000003040552,0.03434531],"study_design_scores_gemma":[0.0003715232,0.0000993002,0.0006166471,0.0002090124,0.0001552562,0.0001364278,0.001916281,0.7772832,0.216518,0.00001696828,0.002104024,0.0005733591],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1824956,0.0001715507,0.8145105,0.0000178922,0.001307592,0.0003843902,0.0001033672,0.0007266179,0.0002825036],"genre_scores_gemma":[0.9968061,0.000117204,0.002552735,0.0000225574,0.00003682208,0.00009221162,0.00005258164,0.00007559582,0.0002442305],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8143105,"threshold_uncertainty_score":0.9999081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0155584954529282,"score_gpt":0.2397043126400236,"score_spread":0.2241458171870954,"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."}}