{"id":"W2914681368","doi":"10.1155/2019/3680181","title":"A Particle Filter Localization Method Using 2D Laser Sensor Measurements and Road Features for Autonomous Vehicle","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Education, Science and Technology; National Research Foundation of Korea; National Research Foundation","keywords":"Particle filter; Position (finance); Computer vision; Grid reference; Computer science; Range (aeronautics); Grid; Filter (signal processing); Artificial intelligence; Feature (linguistics); Laser; Monte Carlo localization; Road surface; Engineering; Geography; Mobile robot; Geodesy; Optics; Aerospace engineering; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004109118,0.0006461716,0.0006169333,0.0009201062,0.0005225425,0.0006306015,0.0006179605,0.000889715,0.0008022132],"category_scores_gemma":[0.0009082452,0.000399865,0.0005709477,0.0008708925,0.0003612495,0.0009616819,0.0004935064,0.0007287696,0.0005459722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005284683,"about_ca_system_score_gemma":0.00112181,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009401601,"about_ca_topic_score_gemma":0.005577229,"domain_scores_codex":[0.9995952,0.00006412691,0.00001824209,0.0001041301,0.0001889146,0.0000294775],"domain_scores_gemma":[0.9997025,0.00009971942,0.00003845083,0.0000292653,0.0001164968,0.00001345781],"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":[0.0001673527,0.0001305664,0.002377674,0.0002696114,0.0001675039,0.0002243445,0.0002540399,0.2830243,0.04057842,0.01370632,0.005703661,0.6533963],"study_design_scores_gemma":[0.00002494911,0.00007408523,0.0008628451,0.00001255929,0.00002709642,0.00009939315,0.00001865043,0.9853988,0.006428039,0.001494263,0.005527849,0.00003153508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002337836,0.0001340666,0.9967614,0.00003835205,0.00005628538,0.0000128123,0.00001536352,0.0002516073,0.000392368],"genre_scores_gemma":[0.2465794,0.0008574317,0.7470381,0.000141245,0.0001872698,0.0002066582,0.0002378517,0.00009694597,0.004655056],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009401601,"threshold_uncertainty_score":0.01869375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700627786446976,"score_gpt":0.2923136623875843,"score_spread":0.2653073845231145,"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."}}