{"id":"W2031081486","doi":"10.1145/2641798.2641807","title":"Lane detection and tracking system based on the MSER algorithm, hough transform and kalman filter","year":2014,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Hough transform; Computer vision; Artificial intelligence; Kalman filter; Computer science; Tracking (education); Pixel; Algorithm; Pattern recognition (psychology); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000175831,0.0000904839,0.00008984586,0.00003651894,0.00009817713,0.0000151263,0.00004352416,0.000107878,0.00001464317],"category_scores_gemma":[0.000003509731,0.00006043644,0.00001601866,0.00004331762,0.00003200744,0.00004594662,0.000004411317,0.0001541011,0.000006972346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001821354,"about_ca_system_score_gemma":0.000001355888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009277356,"about_ca_topic_score_gemma":0.00003327604,"domain_scores_codex":[0.9996337,0.0000156438,0.00008868083,0.0001006616,0.00004311404,0.0001182002],"domain_scores_gemma":[0.9997734,0.00007467667,0.000008462097,0.0001162889,0.000005791758,0.00002139767],"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":[0.00001041769,0.000007523675,0.0001539518,0.00009140747,0.00002393142,0.000002328204,0.0001486563,0.000973355,0.001101438,0.002199812,0.00003487706,0.9952523],"study_design_scores_gemma":[0.0003057934,0.00006850424,0.004497194,0.00002654597,0.00001312878,0.00001728941,0.0001156239,0.9593081,0.03330655,0.0001654686,0.002054555,0.0001212595],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3170449,0.00007360838,0.6581439,0.0007598339,0.0001385262,0.0002777489,0.00000486332,0.001377026,0.02217956],"genre_scores_gemma":[0.9992095,0.000006521523,0.0005962549,0.00008551533,0.00002927335,0.00001241513,8.006734e-7,0.00001354575,0.00004614997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.995131,"threshold_uncertainty_score":0.2464527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005353732338655945,"score_gpt":0.1671099159344176,"score_spread":0.1617561835957617,"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."}}