{"id":"W3096272849","doi":"10.1155/2020/8894060","title":"Vehicle Trajectory Prediction by Knowledge-Driven LSTM Network in Urban Environments","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Youth Innovation Promotion Association; National Key Research and Development Program of China; Hefei Institutes of Physical Science, Chinese Academy of Sciences; Natural Science Foundation of Anhui Province; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Chinese Academy of Sciences","keywords":"Trajectory; Computer science; Knowledge base; A priori and a posteriori; Baseline (sea); Artificial neural network; Artificial intelligence; Machine learning","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002391196,0.0007312626,0.0003636093,0.0003715891,0.0002960632,0.0003968939,0.0008073363,0.0006633365,0.001122572],"category_scores_gemma":[0.0007949451,0.0003725649,0.0003284693,0.0004976419,0.0002662188,0.001106285,0.0004639585,0.0008133752,0.0003433849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007492925,"about_ca_system_score_gemma":0.0007782711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02828447,"about_ca_topic_score_gemma":0.02425403,"domain_scores_codex":[0.9998679,0.00001388101,0.000007285665,0.0000602497,0.00002359856,0.00002711172],"domain_scores_gemma":[0.9998199,0.0000590964,0.00002282236,0.00001834227,0.00006769836,0.00001218601],"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":[0.0001107977,0.00005732862,0.001552401,0.00003609029,0.00003261098,0.0001056821,0.00004509054,0.9011984,0.003581283,0.0008871828,0.001160931,0.09123219],"study_design_scores_gemma":[0.000001186169,0.000006547976,0.0001587147,0.000001364328,0.000003025024,0.000004040848,0.000004038737,0.9987745,0.0005773451,0.0004022455,0.00006504332,0.000001983981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2677552,0.0006680017,0.7220298,0.0003992383,0.00017318,0.00005084228,0.000773855,0.003745167,0.004404607],"genre_scores_gemma":[0.9705728,0.0001823854,0.02655914,0.00005945078,0.00001694066,0.00003529228,0.0007792421,0.00003626654,0.001758582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02828447,"threshold_uncertainty_score":0.05623966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004807795055674757,"score_gpt":0.1869369393667412,"score_spread":0.1821291443110665,"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."}}