{"id":"W2897326341","doi":"10.1109/ivs.2018.8500614","title":"Vehicle Trajectory Prediction with Gaussian Process Regression in Connected Vehicle Environment$\\star$","year":2018,"lang":"en","type":"article","venue":"","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Trajectory; Computer science; Kriging; Gaussian process; Cluster analysis; Process (computing); Ground-penetrating radar; Real-time computing; Vehicle dynamics; Artificial intelligence; Collision avoidance; Kinematics; Data modeling; Collision; Gaussian; Machine learning; Engineering; Radar; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0001832697,0.0002047901,0.0001771144,0.0001436978,0.0001985259,0.0001399409,0.0005899205,0.0001003148,0.0002107677],"category_scores_gemma":[0.00001662138,0.0001429034,0.00002396961,0.0005955706,0.0001540402,0.001010231,0.00008927406,0.0002226255,0.0001122164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006614324,"about_ca_system_score_gemma":0.0001219529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004310254,"about_ca_topic_score_gemma":0.00009111255,"domain_scores_codex":[0.9982789,0.000068047,0.0002680971,0.0006096551,0.0003681851,0.0004071541],"domain_scores_gemma":[0.9991788,0.00003443593,0.000107296,0.0004791345,0.00006069613,0.0001396071],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001598144,0.004481521,0.4993649,0.001052846,0.0002187001,0.0006543713,0.03880335,0.0007502895,0.1263029,0.08935454,0.007371822,0.2300467],"study_design_scores_gemma":[0.004486693,0.003129123,0.5460194,0.0006933241,0.00002328637,0.00009423205,0.0009135911,0.3033819,0.1282919,0.009280401,0.002428995,0.00125711],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.814019,0.00005808869,0.1745176,0.001458355,0.000136688,0.0003476063,0.000004134174,0.0003796789,0.009078841],"genre_scores_gemma":[0.9911178,0.00001310229,0.008241753,0.0002347344,0.00007704805,0.00003329044,0.000003568308,0.00001444681,0.0002643086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3026316,"threshold_uncertainty_score":0.5827433,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00913459223447122,"score_gpt":0.2192752657120637,"score_spread":0.2101406734775925,"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."}}