{"id":"W3045933035","doi":"10.1109/icc40277.2020.9149085","title":"A Novel Lane Departure Warning System for Improving Road Safety","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Lane departure warning system; Computer science; Offset (computer science); Key (lock); Warning system; Advanced driver assistance systems; Intelligent transportation system; Image processing; Real-time computing; Artificial intelligence; Computer vision; Image (mathematics); Transport engineering; Computer security; Engineering; Telecommunications","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":[0.0001677708,0.0005443167,0.0004877048,0.0004869089,0.0004181973,0.0004819096,0.001054241,0.0007194089,0.003209104],"category_scores_gemma":[0.0003947374,0.0002745683,0.0003561684,0.0002839805,0.000178214,0.0009575662,0.0006375102,0.0005738628,0.00174243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003032427,"about_ca_system_score_gemma":0.0005882013,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002841314,"about_ca_topic_score_gemma":0.002342232,"domain_scores_codex":[0.999823,0.00001434097,0.000008460913,0.00006171325,0.00006801709,0.00002437888],"domain_scores_gemma":[0.9997736,0.00002109708,0.00001836379,0.00003175575,0.0001268586,0.00002845199],"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":[0.0006988086,0.0004198293,0.003716473,0.0003520114,0.00007021561,0.0005891479,0.0002800191,0.04114946,0.2782317,0.003299249,0.01798288,0.6532101],"study_design_scores_gemma":[0.0001105889,0.0005166061,0.002718306,0.00002444005,0.00008630235,0.00049729,0.00005291519,0.9115486,0.06182967,0.0007608581,0.02176485,0.00008965196],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07452464,0.0005422885,0.9025911,0.000388519,0.0005101548,0.0001848797,0.0002849346,0.01456702,0.006406451],"genre_scores_gemma":[0.8136244,0.0003543111,0.1710274,0.0004008725,0.0001370026,0.0001744449,0.0006071185,0.000139339,0.0135352],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003209104,"threshold_uncertainty_score":0.01073557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009164938222034097,"score_gpt":0.1862290121958954,"score_spread":0.1770640739738613,"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."}}