{"id":"W3197287938","doi":"10.1016/j.aap.2021.106355","title":"Modeling pedestrian behavior in pedestrian-vehicle near misses: A continuous Gaussian Process Inverse Reinforcement Learning (GP-IRL) approach","year":2021,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Reinforcement learning; Microsimulation; Collision avoidance; Computer science; Traffic simulation; Process (computing); Pedestrian crossing; Traffic conflict; Collision; Intersection (aeronautics); Gaussian process; Poison control; Pedestrian detection; Simulation; Artificial intelligence; Engineering; Gaussian; Transport engineering; Computer security; Traffic congestion","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"],"consensus_categories":[],"category_scores_codex":[0.0004542898,0.0002898037,0.0005226613,0.0003556901,0.0001929525,0.0002001999,0.0002111649,0.0001833884,0.0002442002],"category_scores_gemma":[0.00005658226,0.0003168745,0.0004318515,0.001630743,0.0000199561,0.0004708708,0.00006011215,0.0004239056,0.00002717997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001894153,"about_ca_system_score_gemma":0.0001100004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003132069,"about_ca_topic_score_gemma":0.003352952,"domain_scores_codex":[0.997714,0.0001434172,0.0008018478,0.0004820235,0.0004111978,0.0004475284],"domain_scores_gemma":[0.9992601,0.00001979857,0.000123818,0.0003470713,0.000106873,0.0001422952],"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":[0.00001889632,0.0001246713,0.1306594,0.00003002982,0.0003338954,0.00003609274,0.000855407,0.8649025,0.000135003,0.00001088884,0.00001600461,0.002877195],"study_design_scores_gemma":[0.00100662,0.00003559625,0.02347254,0.00006622403,0.001410033,0.000007611322,0.00318546,0.9701726,0.0001846275,0.000027209,0.00007150546,0.0003599052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9196084,0.0004288807,0.078117,0.00003258336,0.00007923185,0.0003568363,2.425966e-7,0.0002434951,0.001133314],"genre_scores_gemma":[0.9970876,0.0002527665,0.001106784,0.00001173923,0.00006610643,0.0001104209,0.0003671749,0.00004197712,0.0009554078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1071869,"threshold_uncertainty_score":0.9999284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0160325258043774,"score_gpt":0.2591965520299438,"score_spread":0.2431640262255664,"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."}}