{"id":"W4389356553","doi":"10.5194/isprs-annals-x-1-w1-2023-635-2023","title":"VANISHING POINT AIDED LANE DETECTION USING A MULTI-SENSOR SYSTEM","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"RANSAC; Artificial intelligence; Computer science; Hough transform; Computer vision; Benchmark (surveying); Outlier; Lidar; Vanishing point; Point (geometry); Point cloud; Suite; Line (geometry); Image (mathematics); Remote sensing; Geography; Mathematics","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":[0.0008893912,0.0001250487,0.0001818154,0.0003012636,0.000432093,0.00009697394,0.0001521625,0.0001155774,7.930754e-7],"category_scores_gemma":[0.0001446519,0.00009607464,0.00007226472,0.0008941873,0.0002037761,0.000259246,0.00007041039,0.0001639894,0.000007035174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000024171,"about_ca_system_score_gemma":0.00001888881,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03334633,"about_ca_topic_score_gemma":0.003188335,"domain_scores_codex":[0.9989683,0.00004506081,0.0003866713,0.0001059526,0.0002230428,0.0002709503],"domain_scores_gemma":[0.9994718,0.00006412005,0.0001637038,0.0001688535,0.00008698871,0.00004447075],"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":[0.000008545074,0.000002088706,0.0001003873,0.00009354644,0.00002048992,9.038721e-7,0.0008281789,0.02912318,0.009231299,0.00000500478,0.00001591474,0.9605705],"study_design_scores_gemma":[0.0001143903,0.00002589657,0.001236174,0.00009142782,0.000007789621,0.00003524122,0.001868277,0.929818,0.06646679,0.00007655227,0.0001526339,0.0001068076],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5430996,0.00002190459,0.4556136,0.0001540082,0.0003716619,0.0001376969,0.000006296762,0.0003839646,0.0002113173],"genre_scores_gemma":[0.9982499,0.00003322631,0.001614495,0.00006648245,0.00002181326,1.211785e-7,0.00000169301,0.000007018142,0.000005225754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9604636,"threshold_uncertainty_score":0.9730907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05312428250316076,"score_gpt":0.2834202404306567,"score_spread":0.2302959579274959,"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."}}