{"id":"W3119361198","doi":"10.1109/iv47402.2020.9304591","title":"Do They Want to Cross? Understanding Pedestrian Intention for Behavior Prediction","year":2020,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pedestrian; Anticipation (artificial intelligence); Computer science; Task (project management); Trajectory; Action (physics); Scale (ratio); Work (physics); Point (geometry); Estimation; Human behavior; Artificial intelligence; Machine learning; Human–computer interaction; Transport engineering; Engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007938275,0.0003344635,0.0002164491,0.0005102234,0.0001146469,0.0006342186,0.0001728629,0.0003583135,0.001110956],"category_scores_gemma":[0.004526929,0.0001926576,0.0003100005,0.0002347794,0.0001700353,0.0006254531,0.0002171832,0.0003519358,0.0003718699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001358374,"about_ca_system_score_gemma":0.0001957775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005950375,"about_ca_topic_score_gemma":0.007172822,"domain_scores_codex":[0.9998451,0.00008073966,0.000005789288,0.00003126071,0.00001930002,0.00001772876],"domain_scores_gemma":[0.9979243,0.001376896,0.0002786783,0.0001116056,0.0001891143,0.0001192988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000784445,0.0006979255,0.8449258,0.0001647225,0.000173197,0.0001251475,0.001637253,0.01156207,0.01077032,0.000718235,0.00108644,0.1273545],"study_design_scores_gemma":[0.00002494013,0.0005667394,0.5961308,0.00005141208,0.0001309818,0.0001753028,0.00115115,0.395498,0.003147345,0.002412908,0.0006612276,0.00004902337],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838726,0.000111981,0.01467897,0.0001094631,0.000008359932,0.00001962421,0.0001463171,0.00009261251,0.0009601141],"genre_scores_gemma":[0.996639,0.0000463817,0.003043019,0.00001157577,0.000004035186,0.000007219669,0.000101349,0.000004561259,0.0001428542],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005950375,"threshold_uncertainty_score":0.01183146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06394491759693305,"score_gpt":0.2703499997218086,"score_spread":0.2064050821248755,"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."}}