{"id":"W3096107788","doi":"10.1016/j.aap.2020.105842","title":"In-vehicle displays to support driver anticipation of traffic conflicts in automated vehicles","year":2020,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Anticipation (artificial intelligence); Event (particle physics); Driving simulator; Cruise control; Automation; Baseline (sea); Transport engineering; Computer science; Control (management); Advanced driver assistance systems; Computer security; Engineering; Simulation; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000380628,0.0001275913,0.0003550215,0.0007409871,0.00003030142,0.00002703055,0.0001578544,0.00009696301,0.008914694],"category_scores_gemma":[0.00005409799,0.000140549,0.0002140665,0.001711333,0.00001615253,0.0002923851,0.00003472809,0.0001197897,0.0006132581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006618191,"about_ca_system_score_gemma":0.00001777309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002450041,"about_ca_topic_score_gemma":0.003003694,"domain_scores_codex":[0.9980788,0.0002934862,0.0008396253,0.0003445039,0.0002451098,0.0001985031],"domain_scores_gemma":[0.9992657,0.00005820459,0.0002871567,0.000214004,0.00008003449,0.00009494997],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003667989,0.0009637039,0.9050467,0.00001321015,0.0009573457,0.00004590566,0.04199871,0.03008701,0.004799719,0.001282217,0.005432541,0.009006159],"study_design_scores_gemma":[0.0009043816,0.0001368615,0.9509902,0.00002192434,0.0002383218,7.29253e-7,0.0008643437,0.04570736,0.0003822857,0.00001629328,0.0006053383,0.0001319438],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965807,0.00001449918,0.000838205,0.0008446104,0.0001442709,0.0003035083,0.000001102671,0.0001079517,0.001165177],"genre_scores_gemma":[0.9984094,0.00000490413,0.00005765654,0.000386072,0.00003235937,0.00005229017,0.0001127538,0.00001148118,0.0009331091],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04594354,"threshold_uncertainty_score":0.9919913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04331451019564544,"score_gpt":0.3997367976184822,"score_spread":0.3564222874228368,"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."}}