{"id":"W2121457600","doi":"","title":"Reducing multipath effects in vehicle localization by fusing GPS with machine vision","year":2009,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Global Positioning System; Computer science; Multipath propagation; Computer vision; Kalman filter; Simultaneous localization and mapping; Artificial intelligence; Machine vision; Visibility; Map matching; Intelligent transportation system; Real-time computing; Assisted GPS; Mobile robot; Engineering; Robot; Telecommunications; Geography","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":[0.0005056442,0.0008277537,0.0005723389,0.001017248,0.0002795607,0.0005216937,0.0005675085,0.0009436116,0.0006852183],"category_scores_gemma":[0.002375424,0.0005086266,0.0005695497,0.001042578,0.0003445195,0.001481429,0.001122881,0.0005153723,0.0003422936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000323376,"about_ca_system_score_gemma":0.0004035902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003222074,"about_ca_topic_score_gemma":0.003117672,"domain_scores_codex":[0.9995275,0.00009236687,0.00002047968,0.00008669413,0.0002065658,0.00006634701],"domain_scores_gemma":[0.9991906,0.0003588824,0.0001219033,0.0001173941,0.0001887643,0.00002248868],"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":[0.0003512519,0.0001291651,0.004696844,0.0001665962,0.0001865157,0.0003055813,0.0001716094,0.3152916,0.08928239,0.002362998,0.0009512923,0.5861042],"study_design_scores_gemma":[0.00003239656,0.0003472021,0.005201298,0.00002407574,0.0001234991,0.0003952927,0.00005788428,0.9501243,0.03681635,0.004537083,0.002281622,0.00005908046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09964946,0.001076799,0.8971686,0.0001374711,0.00007890844,0.00002016119,0.00003179699,0.0009247895,0.0009120632],"genre_scores_gemma":[0.7809585,0.0008128558,0.2169627,0.0000852678,0.0001016207,0.00002941857,0.0001090386,0.00006181697,0.0008787416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003222074,"threshold_uncertainty_score":0.006406665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006027331603276832,"score_gpt":0.2282604439937764,"score_spread":0.2222331123904996,"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."}}