{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001080978,0.0001666183,0.0001252976,0.000386326,0.00007787591,0.0001362816,0.0001805455,0.0001180949,0.00007667979],"category_scores_gemma":[0.00005673176,0.0001469687,0.0000216258,0.0002796252,0.00002352239,0.001065182,0.00002057419,0.0001900603,0.00005583991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001725264,"about_ca_system_score_gemma":0.00001745152,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003549262,"about_ca_topic_score_gemma":0.00001132979,"domain_scores_codex":[0.9989918,0.00001744206,0.0003465118,0.0001201302,0.0003554017,0.0001687084],"domain_scores_gemma":[0.9995663,0.00002511929,0.00008641599,0.000135877,0.000149572,0.00003678035],"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":[0.0003097421,0.0001598614,0.002005261,0.0001070101,0.00002257896,0.00001011646,0.002331143,0.4532963,0.02615314,0.03966183,0.003198755,0.4727443],"study_design_scores_gemma":[0.001012323,0.0001839194,0.00222456,0.0003176031,0.000002839226,0.000003804338,0.000144466,0.970343,0.02311372,0.0005713516,0.001874048,0.0002083508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.335554,0.00006561301,0.6200848,0.001168716,0.0007674842,0.0006729778,0.00003839824,0.001188733,0.04045932],"genre_scores_gemma":[0.9986302,0.00008719689,0.0005626895,0.0003364874,0.0000185655,0.00001273743,0.0003135546,0.00000927265,0.00002926761],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6630762,"threshold_uncertainty_score":0.5993209,"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."}}