{"id":"W2989704640","doi":"10.1109/cvpr42600.2020.01408","title":"CoverNet: Multimodal Behavior Prediction Using Trajectory Sets","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nutrasource","funders":"","keywords":"Trajectory; Computer science; Set (abstract data type); Probabilistic logic; State (computer science); Frame (networking); State space; Artificial intelligence; Machine learning; Data mining; Algorithm; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005306294,0.001545474,0.0008425889,0.001675693,0.0005010265,0.0007507539,0.001810214,0.0009661323,0.003142455],"category_scores_gemma":[0.003377855,0.0005370626,0.0008549711,0.001248794,0.0004413293,0.001857338,0.001257321,0.001355189,0.001260781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009229563,"about_ca_system_score_gemma":0.001080821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01943969,"about_ca_topic_score_gemma":0.02579571,"domain_scores_codex":[0.9995902,0.00005828727,0.00001693336,0.0001632574,0.0001302609,0.00004112305],"domain_scores_gemma":[0.9992439,0.0002932473,0.00009258941,0.0001630461,0.0001465181,0.0000608603],"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":[0.0002726645,0.0002274136,0.01043362,0.0001892211,0.0001872318,0.0002699699,0.0001661146,0.6997572,0.002141671,0.007973416,0.02439887,0.2539826],"study_design_scores_gemma":[0.000005493426,0.0000138352,0.0003503856,0.000009995045,0.000006215097,0.00002121374,0.00001191396,0.9941607,0.0006240357,0.003210726,0.001579103,0.000006314398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05054635,0.0006964716,0.9206549,0.0005506224,0.0002006447,0.0001957102,0.00735411,0.01623001,0.003571222],"genre_scores_gemma":[0.6484109,0.000545806,0.3221695,0.0002307401,0.0001766149,0.0004478132,0.02240332,0.0008735915,0.00474166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01943969,"threshold_uncertainty_score":0.03865308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02700109941203355,"score_gpt":0.2450044113086378,"score_spread":0.2180033118966042,"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."}}