{"id":"W2076052537","doi":"10.1109/ccece.2014.6901109","title":"Obstacle avoidance in real time with Nonlinear Model Predictive Control of autonomous vehicles","year":2014,"lang":"en","type":"article","venue":"","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Obstacle avoidance; CarSim; Model predictive control; Control theory (sociology); Trajectory; Controller (irrigation); Computer science; Vehicle dynamics; Collision avoidance; Nonlinear system; Obstacle; Control engineering; Nonlinear model; Constraint (computer-aided design); Engineering; Control (management); Mobile robot; Artificial intelligence; Automotive engineering","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":[0.0003006184,0.0004267819,0.0003105486,0.00014149,0.0002242352,0.0004662488,0.0004289471,0.0003242568,0.0005525224],"category_scores_gemma":[0.0009047532,0.0001587453,0.0001793339,0.0001426776,0.0003516856,0.0003448622,0.0004258314,0.0005258118,0.0001424646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002885729,"about_ca_system_score_gemma":0.0006130461,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008485541,"about_ca_topic_score_gemma":0.006176603,"domain_scores_codex":[0.9998193,0.00004187795,0.000007212324,0.00002754434,0.00008175538,0.00002224527],"domain_scores_gemma":[0.9996729,0.0001321907,0.00005464642,0.00003447549,0.00009469848,0.00001105608],"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":[0.0000970582,0.00003749586,0.0003488515,0.00007371268,0.00001351859,0.00007008329,0.00007485364,0.9576604,0.007330909,0.001652895,0.0003980675,0.03224208],"study_design_scores_gemma":[0.000006544733,0.00003700496,0.00012539,0.000002407455,0.000003108204,0.000008559467,0.000005137677,0.9979668,0.001220728,0.0003252066,0.000296772,0.000002386313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1210303,0.0003658069,0.8699945,0.0001532159,0.00007336098,0.00005359844,0.00003581956,0.001263158,0.007030271],"genre_scores_gemma":[0.9822215,0.00008495619,0.01604245,0.00002307199,0.00001027134,0.00005518597,0.00003260783,0.00001820468,0.001511794],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008485541,"threshold_uncertainty_score":0.01687235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002876358691188529,"score_gpt":0.1674413862694296,"score_spread":0.1645650275782411,"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."}}