{"id":"W2943607143","doi":"10.1117/12.2521694","title":"Performance evaluation of neural network based integration of vision and motion sensors for vehicular navigation","year":2019,"lang":"en","type":"article","venue":"","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Military College of Canada; Queen's University","funders":"","keywords":"GNSS applications; Computer science; Air navigation; Gyroscope; Inertial navigation system; Odometry; Inertial measurement unit; Real-time computing; Multipath propagation; BeiDou Navigation Satellite System; Satellite system; Sensor fusion; Artificial intelligence; Computer vision; Global Positioning System; Orientation (vector space); Engineering; Telecommunications; Mobile robot; Robot","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":[0.000442394,0.00007068038,0.0001067869,0.00004233805,0.00001915507,0.000005219758,0.0000206345,0.00006201593,0.00001673688],"category_scores_gemma":[0.00001602287,0.00006167407,0.00003182939,0.0001144844,0.00001066622,0.0001796637,0.00000273285,0.00003780054,0.000001282568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002994285,"about_ca_system_score_gemma":0.000005103223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001042325,"about_ca_topic_score_gemma":0.000002265026,"domain_scores_codex":[0.9993944,0.00003218502,0.0002155659,0.00008143573,0.0002018769,0.00007449061],"domain_scores_gemma":[0.9995588,0.00003842106,0.00005949881,0.0000822691,0.0002475089,0.00001348256],"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":[0.00002682047,0.000005427918,0.001585587,0.00009820852,0.00000404103,6.003457e-9,0.00004628846,0.7994326,0.1653862,0.00006911341,0.00001238531,0.03333323],"study_design_scores_gemma":[0.000462574,0.0001011109,0.03451922,0.00007392683,0.00002793276,2.892885e-7,0.00001625728,0.8143108,0.1503905,0.00004045794,0.000005666443,0.00005128129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912707,0.00003457411,0.00768656,0.00001146252,0.0001842464,0.0005188864,0.00000256822,0.00003279198,0.0002582248],"genre_scores_gemma":[0.9986131,0.000004772038,0.00118358,0.000003789422,0.00003981107,0.000009731624,0.0001279049,0.00001001327,0.000007281438],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03328195,"threshold_uncertainty_score":0.2514996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213039079144367,"score_gpt":0.2472964067281606,"score_spread":0.235166015936717,"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."}}