{"id":"W3206335241","doi":"10.1109/tvt.2021.3120214","title":"Robustness Improvement of Using Pre-Trained Network in Visual Odometry for On-Road Driving","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Visual odometry; Artificial intelligence; Computer science; Artificial neural network; Odometry; Machine learning; Computer vision; Robot; Mobile 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":[0.00009989584,0.0001567908,0.0002624822,0.0004075715,0.00006838838,0.00001050266,0.00008874681,0.0002874339,0.00001790162],"category_scores_gemma":[0.00001165779,0.0001788173,0.00009400418,0.000922641,0.00004146522,0.00003783714,0.000001396675,0.0002372742,5.03164e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001240215,"about_ca_system_score_gemma":0.000031861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006839141,"about_ca_topic_score_gemma":0.00005358304,"domain_scores_codex":[0.9990274,0.00001595092,0.00032711,0.0002276884,0.0001160305,0.0002857956],"domain_scores_gemma":[0.9995587,0.00005426119,0.00004062475,0.000238507,0.00007898294,0.0000289056],"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":[0.00001688065,0.0001305035,0.00003673743,0.0000669439,0.00005560774,0.00000863995,0.00001240987,0.8963523,0.08847389,0.0001116546,0.00000516683,0.01472928],"study_design_scores_gemma":[0.0005135391,0.0001549957,0.00005808158,0.0001043081,0.00002865497,0.000004881278,0.00005193773,0.7674525,0.2314175,0.00006185276,0.00002160903,0.0001300915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4364791,0.00004061498,0.5629148,0.00004150788,0.0002364784,0.0001687588,0.000004124232,0.0001060872,0.000008505391],"genre_scores_gemma":[0.9907038,0.00003560942,0.009100964,0.00002082127,0.00002674767,0.00004470571,0.000005272993,0.00004196905,0.00002010256],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5542247,"threshold_uncertainty_score":0.7291957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01010256144955336,"score_gpt":0.2469424093724582,"score_spread":0.2368398479229049,"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."}}