{"id":"W3129382303","doi":"10.1002/aisy.202000229","title":"Human–Machine Collaboration for Automated Driving Using an Intelligent Two‐Phase Haptic Interface","year":2021,"lang":"en","type":"article","venue":"Advanced Intelligent Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"State Key Laboratory of Automotive Safety and Energy; National Natural Science Foundation of China; Nanyang Technological University","keywords":"Haptic technology; Human–machine system; Computer science; Interface (matter); Human–computer interaction; Control (management); Smoothness; Torque; Driving simulator; Human–machine interface; Automation; Steering wheel; Simulation; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005299517,0.0004053827,0.0005610705,0.0002878356,0.0004657365,0.0002817841,0.0002865567,0.0001713938,0.00183391],"category_scores_gemma":[0.0001822185,0.0004304285,0.0001750719,0.0004995285,0.00006224024,0.0006047555,0.0000596748,0.0002679123,0.000383495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005303022,"about_ca_system_score_gemma":0.0001041221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001307002,"about_ca_topic_score_gemma":0.0003005665,"domain_scores_codex":[0.9964415,0.0004976792,0.001412122,0.0008151131,0.0003096251,0.0005239859],"domain_scores_gemma":[0.9972038,0.0002102113,0.0005955704,0.0008022831,0.0009430962,0.0002450386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006191703,0.004494446,0.0004759188,0.0004894299,0.00119036,0.0001290841,0.02936929,0.415874,0.4318872,0.09190734,0.006050149,0.01751357],"study_design_scores_gemma":[0.002544993,0.000649063,0.00005191603,0.0004350796,0.0001082604,0.0001838912,0.02660399,0.853323,0.03379146,0.0002140755,0.08134264,0.00075165],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3220809,0.001214135,0.6619938,0.0001353525,0.008920868,0.001636713,0.0001014973,0.00127558,0.002641122],"genre_scores_gemma":[0.9915642,0.00001554445,0.002354257,0.0001685505,0.0003188069,0.0002489799,0.0003388591,0.0001005554,0.004890226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6694832,"threshold_uncertainty_score":0.9998147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06437668381603019,"score_gpt":0.4604792289181868,"score_spread":0.3961025451021566,"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."}}