{"id":"W2785726416","doi":"10.1109/ascc.2017.8287101","title":"Anti-jerk model predictive cruise control for connected electric vehicles with changing road conditions","year":2017,"lang":"en","type":"article","venue":"","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cruise control; Jerk; Vehicle dynamics; Automotive engineering; Model predictive control; Powertrain; Controller (irrigation); Control theory (sociology); Electric vehicle; Torque; Computer science; Road traffic control; Engineering; Control (management)","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":[0.0001296109,0.0001983672,0.0003305755,0.000136453,0.0004978243,0.0001727727,0.0002326875,0.00009171996,0.00000751779],"category_scores_gemma":[0.00003008861,0.0001695523,0.00008095212,0.00007541118,0.00003332653,0.0002387359,0.00001361094,0.0001070042,0.000006639365],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005521115,"about_ca_system_score_gemma":0.00003309782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003955952,"about_ca_topic_score_gemma":0.00004957941,"domain_scores_codex":[0.9989916,0.00001124071,0.0001956713,0.0002153597,0.0001290535,0.0004570761],"domain_scores_gemma":[0.9992316,0.00005957271,0.00008318436,0.0003789308,0.0001490597,0.00009768844],"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":[0.0001296248,0.0000561691,0.001634166,0.00008546802,0.0006282688,0.000009158874,0.0002223934,0.9303729,0.04848874,0.01571532,0.00043858,0.002219241],"study_design_scores_gemma":[0.002831806,0.0001019517,0.004673806,0.00003973711,0.0000835184,0.000006655056,0.00006998938,0.9910707,0.0004719114,0.000370432,0.00003552476,0.0002440045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5020345,0.0001037443,0.491199,0.0001383783,0.000111435,0.0009720236,0.0002292584,0.0004380664,0.004773611],"genre_scores_gemma":[0.9987205,0.0000113015,0.0002216451,0.0000523425,0.0001247195,0.0004281994,0.00002470256,0.00005541227,0.00036113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.496686,"threshold_uncertainty_score":0.6914144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007037046271350929,"score_gpt":0.2094452473924777,"score_spread":0.2024082011211268,"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."}}