{"id":"W7136135934","doi":"10.1109/itsc60802.2025.11423815","title":"Optimizing Pedestrian Safety in Real-Time: An Extreme Value Theory-Based Reinforcement Learning Framework","year":2025,"lang":"","type":"article","venue":"","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Pedestrian; Reinforcement learning; Value (mathematics); Extreme learning machine; Control (management); Event (particle physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001638093,0.001165468,0.001430326,0.0004960771,0.0003204012,0.001078733,0.001652317,0.001417679,0.00191083],"category_scores_gemma":[0.003058888,0.0005044261,0.0008093659,0.0003782618,0.001262514,0.0006878275,0.001168901,0.001583611,0.0002587652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001111608,"about_ca_system_score_gemma":0.001314174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008542538,"about_ca_topic_score_gemma":0.004109773,"domain_scores_codex":[0.9992575,0.0003136009,0.00002906665,0.0001370165,0.0001458818,0.000116822],"domain_scores_gemma":[0.9984036,0.0009801014,0.0001992797,0.0000376193,0.0002656031,0.0001138288],"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":[0.00002003798,0.00002545762,0.0003476338,0.00002213094,0.00001958911,0.00004519989,0.00001994882,0.9910203,0.0002121819,0.003319845,0.0001681735,0.004779674],"study_design_scores_gemma":[0.000005141407,0.00001780815,0.00004031411,0.000002903712,0.00000397057,0.000003901664,0.000002755503,0.9984161,0.0000389588,0.001389958,0.00007567515,0.000002490052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02540192,0.0003963277,0.9696363,0.0003576219,0.00005779904,0.00005011894,0.00003341625,0.0001943833,0.00387203],"genre_scores_gemma":[0.9511597,0.0002651247,0.04474408,0.0001839317,0.00006370935,0.0001682932,0.00006104315,0.00003591301,0.003318263],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008542538,"threshold_uncertainty_score":0.01698565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02606030031209262,"score_gpt":0.2863342966971705,"score_spread":0.2602739963850779,"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."}}