{"id":"W4384161746","doi":"10.1109/tits.2023.3291196","title":"Pedestrian Trajectory Prediction in Pedestrian-Vehicle Mixed Environments: A Systematic Review","year":2023,"lang":"en","type":"review","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Pedestrian; Trajectory; Computer science; Machine learning; Artificial intelligence; Motion (physics); Data mining; Transport engineering; Engineering","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007894896,0.0008696411,0.003044026,0.0009811361,0.0001352543,0.00003966436,0.0004673529,0.0009837161,0.00007543481],"category_scores_gemma":[0.000009787058,0.0008419866,0.000842818,0.001216253,0.00007431139,0.0002000482,4.709494e-7,0.001281659,0.001257277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008208068,"about_ca_system_score_gemma":0.0001210978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007492999,"about_ca_topic_score_gemma":0.0003078416,"domain_scores_codex":[0.9944905,0.0003898342,0.003377912,0.0007009436,0.000505291,0.0005354923],"domain_scores_gemma":[0.9982201,0.0003575606,0.0004431758,0.0007960551,0.00002760939,0.0001555185],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.000007331168,0.0001382205,9.80596e-7,0.9474585,0.0006920252,0.00005494289,0.0001807113,0.03318589,0.000001373563,0.00002274329,0.0001218787,0.01813542],"study_design_scores_gemma":[0.0005554454,0.0001763494,0.000008156027,0.8973852,0.005766068,0.00005710547,0.0004610007,0.004476113,0.00005609008,0.000009145568,0.08963931,0.001410024],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00001533497,0.8561488,0.133981,0.000006549717,0.001983827,0.00567332,0.0008488343,0.001285153,0.00005714543],"genre_scores_gemma":[0.003742459,0.989867,0.00001156214,0.000009886483,0.00005019428,0.004794641,0.0003281888,0.0002395528,0.000956563],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.1339694,"threshold_uncertainty_score":0.9995204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04420379350282414,"score_gpt":0.2710701641497105,"score_spread":0.2268663706468863,"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."}}