{"id":"W3046603013","doi":"10.1155/2020/8859689","title":"UB-LSTM: A Trajectory Prediction Method Combined with Vehicle Behavior Recognition","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Australian Research Council; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Acceleration; Trajectory optimization; Control theory (sociology); Artificial intelligence; Simulation; Algorithm; Control (management)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009600334,0.0001120505,0.0001994594,0.00007587028,0.00004010326,0.00000510985,0.00006828387,0.00009982252,0.00002692804],"category_scores_gemma":[0.00000671729,0.0001034321,0.00006532777,0.0001977332,0.00002224692,0.0004161315,5.527367e-7,0.0003397145,0.000003443911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004261326,"about_ca_system_score_gemma":0.00002439119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.863878e-7,"about_ca_topic_score_gemma":0.00001075475,"domain_scores_codex":[0.9992359,0.00001847902,0.0003921892,0.00009437679,0.0001450017,0.0001140624],"domain_scores_gemma":[0.999589,0.00002602124,0.0001489203,0.00005533348,0.0001044263,0.00007634772],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002303073,0.0001871411,0.00916528,0.0001888774,0.0002319899,0.0002109696,0.004821365,0.5246831,0.2186461,0.00009743056,0.00009018795,0.2393745],"study_design_scores_gemma":[0.007868599,0.004148552,0.847127,0.000186483,0.0007016813,0.00008846044,0.001666766,0.01265715,0.1232799,0.0006501634,0.001124173,0.0005010529],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8834984,0.0000928934,0.1156436,0.0001774608,0.0001366998,0.0001564403,0.00002756599,0.0002183455,0.00004855123],"genre_scores_gemma":[0.9647971,0.0000800847,0.03493419,0.0000508551,0.00005982289,0.00001322574,0.00003743075,0.00002486088,0.000002373632],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8379617,"threshold_uncertainty_score":0.421784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01121256949827227,"score_gpt":0.2224824401525719,"score_spread":0.2112698706542996,"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."}}