{"id":"W4411154120","doi":"10.3390/vehicles7020057","title":"Navigating Uncertainty: Advanced Techniques in Pedestrian Intention Prediction for Autonomous Vehicles—A Comprehensive Review","year":2025,"lang":"en","type":"article","venue":"Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Pedestrian; Computer science; Data science; Artificial intelligence; Human–computer interaction; Transport engineering; 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":[],"consensus_categories":[],"category_scores_codex":[0.0002623123,0.0002176548,0.0003732957,0.0001117897,0.0001201118,0.00001655874,0.0002079212,0.0002410274,0.000005454559],"category_scores_gemma":[0.00007800432,0.0002352522,0.0001049983,0.0004385165,0.00007939967,0.000170538,0.0000508024,0.0004955983,0.000006247893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002638034,"about_ca_system_score_gemma":0.00004260552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003851318,"about_ca_topic_score_gemma":0.00004451858,"domain_scores_codex":[0.9987128,0.000040279,0.0005491733,0.0002959256,0.0000773228,0.0003245398],"domain_scores_gemma":[0.9993839,0.0001383373,0.00007544929,0.0002737192,0.00009507596,0.00003349757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004199804,0.00004560089,0.002470062,0.002300721,0.00005407141,0.000004680589,0.00008966333,0.00279448,0.01320297,0.001376947,0.0008675848,0.9767512],"study_design_scores_gemma":[0.005883168,0.0007520934,0.0491826,0.0385563,0.0003902657,0.00004880959,0.001717905,0.3775387,0.1365775,0.03097819,0.3562134,0.002161083],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9318576,0.03729251,0.01811031,0.002977656,0.0005531317,0.003104006,0.00009727731,0.004556903,0.001450643],"genre_scores_gemma":[0.9876217,0.007118412,0.004078992,0.0004101284,0.0000320827,0.0005648308,0.00008139409,0.00003462849,0.00005783413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9745901,"threshold_uncertainty_score":0.9593306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01189006490819107,"score_gpt":0.2787939145153639,"score_spread":0.2669038496071729,"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."}}