{"id":"W4200277452","doi":"10.1109/vtc2021-fall52928.2021.9625310","title":"SA-SGAN: A Vehicle Trajectory Prediction Model Based on Generative Adversarial Networks","year":2021,"lang":"en","type":"article","venue":"2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Science and Technology Department, Henan Province; National Natural Science Foundation of China","keywords":"Trajectory; Computer science; Sequence (biology); Generative grammar; Task (project management); Artificial intelligence; Displacement (psychology); Generative model; Adversarial system; Machine learning; Motion (physics); Engineering","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":[0.0006397826,0.0008548558,0.0006375569,0.0004667501,0.0003021015,0.0004496912,0.001640289,0.0008079343,0.001506846],"category_scores_gemma":[0.001731504,0.0004119818,0.0006459986,0.0004148123,0.0005751964,0.0009212089,0.0008627611,0.001554095,0.0004407919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008898138,"about_ca_system_score_gemma":0.0006903292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01553296,"about_ca_topic_score_gemma":0.01518364,"domain_scores_codex":[0.9996992,0.00008307286,0.00001159354,0.00009939448,0.00006508044,0.00004174103],"domain_scores_gemma":[0.9993619,0.0003366005,0.00008232058,0.00005180069,0.000131356,0.00003594866],"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":[0.00003462486,0.00001715715,0.0008092729,0.00001234685,0.0000209193,0.00003631683,0.00001963924,0.9805361,0.0004721446,0.002758176,0.001129793,0.01415358],"study_design_scores_gemma":[8.43408e-7,0.000004282077,0.00004696826,0.000001144398,0.000001863859,0.000004915028,9.771117e-7,0.9990793,0.00009287192,0.0006610521,0.0001042646,0.000001370711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04523801,0.0005985789,0.9480166,0.0006708895,0.0001479095,0.00005838187,0.0004531135,0.001219396,0.003597081],"genre_scores_gemma":[0.9288178,0.0004384757,0.06179676,0.0003279218,0.00008192316,0.000129994,0.001140865,0.0001166695,0.007149651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01553296,"threshold_uncertainty_score":0.0308851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009742288950092161,"score_gpt":0.2003179927223472,"score_spread":0.1905757037722551,"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."}}