{"id":"W3037001428","doi":"10.1177/0954407020929233","title":"Real-time estimation of tire–road friction coefficient based on lateral vehicle dynamics","year":2020,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":69,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"CarSim; Kalman filter; Control theory (sociology); Vehicle dynamics; Axle; Slip (aerodynamics); MATLAB; Engineering; Computer science; Automotive engineering; Structural engineering; Artificial intelligence","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":[0.0004143493,0.0002032091,0.0004907871,0.0001721784,0.00002965916,0.00001522675,0.0003197995,0.0001319965,0.000008583229],"category_scores_gemma":[0.0002412886,0.0001724502,0.0002493247,0.0004413724,0.00003039291,0.0001905734,0.00003005652,0.0002976971,0.000001339725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001622949,"about_ca_system_score_gemma":0.00004988188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004713558,"about_ca_topic_score_gemma":1.299204e-7,"domain_scores_codex":[0.998245,0.000007020904,0.0009325355,0.0001193138,0.0005050272,0.0001911059],"domain_scores_gemma":[0.9990454,0.00005745656,0.000412564,0.00009720319,0.0002685776,0.0001188079],"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":[0.00004973443,0.00004688436,0.0000136466,0.0003904506,0.00005977965,7.517506e-7,0.00007760579,0.8676724,0.1275316,0.003179494,0.00003851342,0.0009391209],"study_design_scores_gemma":[0.000717523,0.0003382284,0.000125556,0.0006118691,0.00006341758,0.000008179925,0.00003014183,0.955577,0.04234804,0.000009975487,0.00003756865,0.0001324971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9471751,0.00004256063,0.05117615,0.000150774,0.0007567067,0.00028905,0.00002995653,0.0001219699,0.0002577039],"genre_scores_gemma":[0.9986459,0.00002300031,0.001191094,0.000006723719,0.00009028874,0.000006294607,0.000002541388,0.00003051658,0.000003625243],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0879046,"threshold_uncertainty_score":0.7032315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004742653856700492,"score_gpt":0.1818167608079451,"score_spread":0.1770741069512446,"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."}}