{"id":"W2991157920","doi":"10.3390/s19235231","title":"Cooperative Localization Improvement Using Distance Information in Vehicular Ad Hoc Networks","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Global Positioning System; Computer science; Kalman filter; Vehicular ad hoc network; Position (finance); Wireless ad hoc network; Sensor fusion; Real-time computing; Trajectory; Track (disk drive); Data mining; Artificial intelligence; Wireless; 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":[0.0009051684,0.0008203748,0.0006572022,0.001263718,0.0004491071,0.0005082691,0.001344298,0.0004661941,0.0003316202],"category_scores_gemma":[0.003184773,0.0002715429,0.0004021383,0.001215365,0.0004201098,0.001165289,0.00139423,0.0004071333,0.0002130908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000562851,"about_ca_system_score_gemma":0.0005602821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00405203,"about_ca_topic_score_gemma":0.003533713,"domain_scores_codex":[0.9988214,0.0002564849,0.00006045431,0.0002909205,0.0004673545,0.0001033596],"domain_scores_gemma":[0.9985851,0.0004626432,0.0002306644,0.0002127813,0.0004561474,0.00005262715],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002856553,0.0001202572,0.004779208,0.0001634866,0.0001327551,0.0001843397,0.0004064534,0.5122492,0.03375278,0.004363923,0.001363741,0.4421982],"study_design_scores_gemma":[0.00003705144,0.0003333844,0.002105009,0.00001633788,0.0000806965,0.000205168,0.0001328508,0.971418,0.01920744,0.002272914,0.004146101,0.00004516504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08972707,0.001261238,0.9061102,0.000105739,0.00007197999,0.00003902717,0.00002968934,0.001017572,0.001637414],"genre_scores_gemma":[0.8849791,0.000383093,0.1128576,0.00006869288,0.0000404731,0.00003870156,0.00008902016,0.00003840529,0.001504898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00405203,"threshold_uncertainty_score":0.008056939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004155638147319215,"score_gpt":0.1898416904847544,"score_spread":0.1856860523374352,"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."}}