{"id":"W3174203047","doi":"10.15607/rss.2021.xvii.029","title":"Radar Odometry Combining Probabilistic Estimation and Unsupervised Feature Learning","year":2021,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Odometry; Probabilistic logic; Pattern recognition (psychology); Feature (linguistics); Radar; Unsupervised learning; Mobile robot; Robot","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005687656,0.001187761,0.000931699,0.001204719,0.0003438059,0.000728055,0.0021207,0.0006867763,0.001044021],"category_scores_gemma":[0.002485494,0.0007760795,0.0008668457,0.00150563,0.0006728118,0.001765714,0.002019933,0.001712258,0.001037658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005202231,"about_ca_system_score_gemma":0.001067305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007530683,"about_ca_topic_score_gemma":0.01243511,"domain_scores_codex":[0.999238,0.0000723843,0.00002938289,0.0003432042,0.0002326805,0.00008429799],"domain_scores_gemma":[0.9989579,0.0002448683,0.0001594504,0.0003233858,0.0002762328,0.00003815571],"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":[0.0001131028,0.0001432701,0.003182791,0.0001022174,0.0001484936,0.00006779691,0.00008685494,0.4069641,0.01204957,0.005100291,0.004745573,0.567296],"study_design_scores_gemma":[0.000009820452,0.00003608577,0.0009366461,0.000010914,0.00001287934,0.00004401809,0.000009875383,0.9890075,0.003731437,0.004580314,0.001604295,0.00001615221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006395904,0.00007592538,0.9907159,0.00004653219,0.00002867621,0.00001911322,0.0001649727,0.001914252,0.0006387839],"genre_scores_gemma":[0.4745567,0.0002031775,0.5188565,0.000169755,0.0001431927,0.000119105,0.002337037,0.0004600276,0.003154406],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007530683,"threshold_uncertainty_score":0.0149737,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005271317232354826,"score_gpt":0.1915073177536094,"score_spread":0.1862360005212546,"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."}}