{"id":"W2035665425","doi":"10.1145/2386958.2386977","title":"Vehicle localization in VANETs using data fusion and V2V communication","year":2012,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Wireless ad hoc network; Sensor fusion; Vehicular ad hoc network; Scheme (mathematics); Information fusion; Computer network; Artificial intelligence; Telecommunications; Wireless","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":[0.0003010565,0.00008003932,0.00008482348,0.00004892441,0.00004307065,0.00002426835,0.0001600087,0.00006963297,0.00003880652],"category_scores_gemma":[0.00001297414,0.00008323634,0.000005654057,0.0001532257,0.00001689607,0.0005447047,0.0001875642,0.0001027961,0.00001270238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004901601,"about_ca_system_score_gemma":0.000004116207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001232117,"about_ca_topic_score_gemma":0.0002808814,"domain_scores_codex":[0.999427,0.0000439664,0.0001457777,0.00009559997,0.00008321186,0.0002044226],"domain_scores_gemma":[0.9993498,0.0000313606,0.0000161939,0.0005327105,0.00001052285,0.00005942543],"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":[0.000002217444,0.00002052022,0.0176739,0.00002448871,0.000006204684,6.301796e-7,0.0002132794,0.9727297,0.001982373,0.0002056117,0.001098187,0.006042905],"study_design_scores_gemma":[0.0001696442,0.000002442391,0.005333802,0.00003361913,0.000007197552,0.000006587169,0.0000404134,0.9887637,0.0002589135,0.00003247961,0.005250197,0.0001010246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9381206,0.003765301,0.05613523,0.00004205449,0.000114156,0.0001796008,0.000004137828,0.0001806774,0.001458211],"genre_scores_gemma":[0.9953308,0.000335914,0.004033617,0.00005468625,0.00004562673,0.000001635666,0.0001652184,0.00002181846,0.00001074949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05721011,"threshold_uncertainty_score":0.3394279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03649670891126605,"score_gpt":0.2575570073593705,"score_spread":0.2210602984481045,"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."}}