{"id":"W2565832396","doi":"10.1109/itsc.2016.7795793","title":"Ecological Adaptive Cruise Control of a plug-in hybrid electric vehicle for urban driving","year":2016,"lang":"en","type":"article","venue":"","topic":"Traffic control and management","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cruise control; Computer science; Cruise; Automotive engineering; Fidelity; Model predictive control; Trajectory; Control (management); SAFER; Hybrid vehicle; Fuel efficiency; Plug-in; Engineering; Power (physics); Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001103548,0.00008918603,0.0001874313,0.00007938455,0.00001133441,0.000004723362,0.00008739185,0.00002661709,0.00005769702],"category_scores_gemma":[0.00003836976,0.00005887132,0.00006509909,0.0000660563,0.00001005527,0.00005063962,0.00001162721,0.00003337726,0.000009358818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006259813,"about_ca_system_score_gemma":0.000007531738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005184885,"about_ca_topic_score_gemma":0.00006488127,"domain_scores_codex":[0.9993732,0.00001127186,0.0001837226,0.000122241,0.00006082121,0.000248753],"domain_scores_gemma":[0.9995974,0.0002330892,0.00001898398,0.00009267651,0.00001956311,0.00003823701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006183115,0.0006927243,0.01145311,0.0001482998,0.0005058653,0.00005118061,0.0001563615,0.04469032,0.1863086,0.0648929,0.01656971,0.6739126],"study_design_scores_gemma":[0.01200542,0.0007286364,0.1865816,0.00006471285,0.00007068993,0.000001729833,0.00003324665,0.7788901,0.007523219,0.00116797,0.01247897,0.0004536441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6395885,0.0002653487,0.3538896,0.000352734,0.0001531782,0.0009173733,0.0000103493,0.0003361677,0.004486797],"genre_scores_gemma":[0.99923,0.00001477665,0.0002213598,0.00003480465,0.00003852027,0.0001207143,2.75063e-7,0.00001117136,0.000328429],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7341998,"threshold_uncertainty_score":0.2400703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00643252875633129,"score_gpt":0.181756755828975,"score_spread":0.1753242270726437,"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."}}