{"id":"W4379184148","doi":"10.1109/ojits.2023.3282237","title":"HISS: A Pedestrian Trajectory Planning Framework Using Receding Horizon Optimization","year":2023,"lang":"en","type":"article","venue":"IEEE Open Journal of Intelligent Transportation Systems","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Trajectory; Pedestrian; Benchmark (surveying); Computer science; Feature (linguistics); Variety (cybernetics); Mathematical optimization; Trajectory optimization; Artificial intelligence; Engineering; Mathematics; Transport 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000840468,0.001026641,0.0008539305,0.0006522674,0.0004564799,0.0009612014,0.00182592,0.0008929329,0.004265836],"category_scores_gemma":[0.001365804,0.0005695359,0.001109898,0.0008086413,0.0005608217,0.0007738958,0.001282943,0.001208975,0.0006934885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001321832,"about_ca_system_score_gemma":0.002482205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02165434,"about_ca_topic_score_gemma":0.0186185,"domain_scores_codex":[0.9995814,0.0001385435,0.0000202658,0.00009020083,0.0001115465,0.00005812162],"domain_scores_gemma":[0.9996655,0.0001276056,0.00005002148,0.00003471923,0.0000727961,0.00004930509],"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":[0.00001556004,0.00001106816,0.0001874665,0.0000236933,0.00001491755,0.0000246482,0.00002071041,0.9786198,0.0001689918,0.01054877,0.001005519,0.009359023],"study_design_scores_gemma":[0.000003055221,0.000006708235,0.00002184126,0.000003825665,0.000002686578,0.000005285836,0.000004870391,0.9954799,0.00007508158,0.003272401,0.001121437,0.000002808023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00324112,0.000159424,0.9930823,0.0001273998,0.00003724797,0.00004734042,0.0003389421,0.0007897594,0.0021765],"genre_scores_gemma":[0.4056172,0.0005540314,0.5861158,0.0001213669,0.00007614122,0.0003828463,0.001662166,0.0004151497,0.005055219],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02165434,"threshold_uncertainty_score":0.04305661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08314008570990604,"score_gpt":0.3296171837102983,"score_spread":0.2464770980003922,"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."}}