{"id":"W2912561618","doi":"10.1109/tits.2019.2894522","title":"Integrated Positioning for Connected Vehicles","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; TD Bank Group; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pseudorange; Global Positioning System; GNSS applications; Multipath propagation; Computer science; Inertial navigation system; Precise Point Positioning; Positioning system; Hybrid positioning system; GPS/INS; Kalman filter; BeiDou Navigation Satellite System; Real-time computing; Inertial measurement unit; Assisted GPS; Engineering; Inertial frame of reference; Telecommunications; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0000936799,0.0002127418,0.0002405202,0.0002825036,0.0001064929,0.00005993538,0.000126505,0.0001875466,0.0001209394],"category_scores_gemma":[0.00000203628,0.0002132668,0.0001371789,0.0003640817,0.00002684254,0.0001579047,6.112975e-8,0.0001786658,0.0001652302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000106467,"about_ca_system_score_gemma":0.00001879685,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003448629,"about_ca_topic_score_gemma":0.00004615013,"domain_scores_codex":[0.9988585,0.00001871791,0.0004862635,0.0002262344,0.000176946,0.0002334125],"domain_scores_gemma":[0.9994059,0.0001158907,0.00004786742,0.0001994246,0.0001805806,0.00005031533],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006107028,0.00004219954,0.0001116646,0.0002445689,0.0001206288,0.00000123298,0.0005560765,0.9813898,0.01102029,0.002458392,0.0001989584,0.003795125],"study_design_scores_gemma":[0.0009490803,0.0002544922,0.000179755,0.0003305402,0.00008752108,0.000005495632,0.003339276,0.3680656,0.6183369,0.0001222113,0.007762752,0.0005663576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1598547,0.0000862073,0.8355606,0.00001660219,0.001931649,0.0008287778,0.0002496995,0.001227419,0.000244404],"genre_scores_gemma":[0.9985294,0.00006451891,0.0004318938,0.00002678847,0.00002344002,0.0002951509,0.0001465568,0.00005593616,0.0004263144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8386747,"threshold_uncertainty_score":0.8696769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01403218807214699,"score_gpt":0.2185269758075546,"score_spread":0.2044947877354077,"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."}}