{"id":"W2770086227","doi":"10.1155/2017/9203251","title":"PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Higher Education Discipline Innovation Project; Ministry of Transport of the People's Republic of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Trajectory; Adaptability; Hardware-in-the-loop simulation; Tracking (education); Simulation; Loop (graph theory); Process (computing); Transmission (telecommunications); Engineering; Vehicle dynamics; Computer science; Control engineering; Real-time computing; Automotive engineering","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":[0.0004089868,0.0001345873,0.0002478661,0.0001071332,0.0002447195,0.00003603872,0.0003536356,0.0001156396,0.000003333797],"category_scores_gemma":[0.00009713804,0.0001160787,0.0001137809,0.00007483776,0.00005121027,0.0007808008,0.000001426337,0.0003702742,0.000001104023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007409209,"about_ca_system_score_gemma":0.00004139825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004433903,"about_ca_topic_score_gemma":0.0001339601,"domain_scores_codex":[0.9989766,0.000007005607,0.0005509798,0.0001037779,0.000132532,0.0002291448],"domain_scores_gemma":[0.9991612,0.0001618407,0.0003319447,0.0002077532,0.0001007027,0.00003660261],"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":[0.0003225221,0.0001211771,0.01712122,0.0002437834,0.00009361769,0.0001996076,0.005986677,0.46759,0.04158852,0.0005971569,0.00002722981,0.4661084],"study_design_scores_gemma":[0.003320673,0.0003001655,0.9679186,0.0002404638,0.00008791148,0.00005271507,0.001064887,0.0114032,0.01162408,0.002699567,0.0009979976,0.0002897263],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9854027,0.0002147409,0.01345606,0.000136517,0.0003043058,0.0002344189,0.00001419384,0.00008128466,0.0001557636],"genre_scores_gemma":[0.9910043,0.00003578372,0.008789843,0.00002329915,0.00009010982,0.00001622749,0.000006259383,0.00002473854,0.000009418243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9507974,"threshold_uncertainty_score":0.4733553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02310765528199139,"score_gpt":0.2567387863299689,"score_spread":0.2336311310479775,"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."}}