{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00005934887,0.0002822546,0.0002961591,0.00008200799,0.00005162854,0.00001803874,0.0002305671,0.0008784782,0.0002060879],"category_scores_gemma":[0.00000704796,0.0003182403,0.0001203249,0.00006292692,0.00005207906,0.00005837767,0.000209656,0.001130216,0.00005896568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002480347,"about_ca_system_score_gemma":0.00006242761,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006724347,"about_ca_topic_score_gemma":0.00001618268,"domain_scores_codex":[0.9989949,0.00001625631,0.0002947668,0.0003431447,0.0001200841,0.000230844],"domain_scores_gemma":[0.9995232,0.0000137067,0.00004488967,0.0003268979,0.00001853401,0.00007276825],"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":[0.00003195601,0.0001241891,0.01380829,0.0005347695,0.0004224587,0.0001381627,0.0007010587,0.9276783,0.0245695,0.0004189965,0.001946962,0.02962533],"study_design_scores_gemma":[0.0002493383,0.00002107069,0.02892504,0.00003524691,0.0001329337,0.00001694141,0.00003198708,0.965488,0.003929186,0.000236922,0.000565532,0.000367765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9370693,0.0002622287,0.05170071,0.0000501654,0.001312386,0.0005535146,0.0002807067,0.004875759,0.003895247],"genre_scores_gemma":[0.9937882,0.00004593065,0.005745291,0.00003190878,0.0001167513,0.00005536289,0.00009908539,0.00006632769,0.00005110033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05671895,"threshold_uncertainty_score":0.999927,"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."}}