{"id":"W4241572027","doi":"10.1002/aisy.202170040","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":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Haptic technology; Computer science; Interface (matter); Human–machine interface; Control (management); Torque; Human–computer interaction; Human–machine system; Automation; Simulation; Engineering; Artificial intelligence; Operating system; Mechanical 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":[0.0004631586,0.000369958,0.0002415378,0.0002182094,0.0003374572,0.0005805101,0.0005274138,0.0005917577,0.002429611],"category_scores_gemma":[0.0008636403,0.0001506205,0.000281254,0.0001127074,0.0003267805,0.000662801,0.0006793219,0.0003649997,0.0003536435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001417974,"about_ca_system_score_gemma":0.0002984073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003631889,"about_ca_topic_score_gemma":0.00038713,"domain_scores_codex":[0.9997068,0.00008587244,0.00001808852,0.0000570101,0.0001016066,0.00003053315],"domain_scores_gemma":[0.9996597,0.0001468608,0.00002974287,0.00004784221,0.00007905815,0.00003679443],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001125212,0.0008918568,0.002468755,0.0003354787,0.0001336555,0.0006636705,0.001340237,0.03164723,0.5523301,0.01472553,0.004053013,0.3902852],"study_design_scores_gemma":[0.0003069056,0.002062755,0.005368982,0.00003420272,0.0001209193,0.0009606077,0.0002887775,0.8573812,0.1022308,0.009288934,0.02185139,0.000104636],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1040989,0.0002470968,0.88928,0.0003221614,0.0001359027,0.0001463914,0.00001974379,0.00116358,0.004586264],"genre_scores_gemma":[0.8587219,0.0001030914,0.1389434,0.00008668693,0.00004231324,0.00008595583,0.0000243808,0.00002015434,0.00197211],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002429611,"threshold_uncertainty_score":0.008127809,"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."}}