{"id":"W2100634055","doi":"10.1109/ivs.2014.6856559","title":"MPC based collaborative adaptive cruise control with rear end collision avoidance","year":2014,"lang":"en","type":"article","venue":"","topic":"Traffic control and management","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Collision avoidance; Cooperative Adaptive Cruise Control; Cruise control; Model predictive control; Collision; Control theory (sociology); Computer science; Actuator; Cruise; Adaptive control; Control (management); PID controller; Control engineering; Engineering; Aerospace engineering; Artificial intelligence","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":[0.0003529586,0.0006413803,0.0005606504,0.0003102986,0.0004763853,0.0005379443,0.001434638,0.0005730439,0.001582328],"category_scores_gemma":[0.000711271,0.0002511918,0.0003603449,0.0002896136,0.0004258363,0.0004512919,0.0008955777,0.0008680968,0.0004185913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002799119,"about_ca_system_score_gemma":0.0005142902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005631603,"about_ca_topic_score_gemma":0.004091169,"domain_scores_codex":[0.9996656,0.00004212312,0.00001256897,0.00008443659,0.0001455036,0.00004984748],"domain_scores_gemma":[0.9996005,0.0001085687,0.00007377538,0.00006267975,0.0001244997,0.00002996802],"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":[0.0001515183,0.00009350168,0.0004802076,0.0001142274,0.00004799326,0.0001999239,0.0001273311,0.8683789,0.01684779,0.005799282,0.001653668,0.1061056],"study_design_scores_gemma":[0.00001336694,0.00008764763,0.0001786127,0.000003361823,0.000008661317,0.0000292291,0.00000632357,0.9959818,0.00185512,0.000627202,0.001202646,0.000006034556],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02394656,0.0002801692,0.9675541,0.00009167993,0.00008165791,0.00004453769,0.00002621554,0.0008949081,0.007080194],"genre_scores_gemma":[0.9604501,0.0001291665,0.03522012,0.00004473178,0.00006344215,0.00006571936,0.00004947156,0.00002923801,0.003948084],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005631603,"threshold_uncertainty_score":0.01119763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003053092490955699,"score_gpt":0.1613591807397542,"score_spread":0.1583060882487985,"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."}}