{"id":"W4403635664","doi":"10.1109/tro.2024.3484634","title":"SICNav: Safe and Interactive Crowd Navigation Using Model Predictive Control and Bilevel Optimization","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Robotics","topic":"Evacuation and Crowd Dynamics","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Artificial Intelligence in Medicine (Canada); University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bilevel optimization; Computer science; Model predictive control; Control (management); Artificial intelligence; Control engineering; Optimization problem; Engineering; Algorithm","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":[0.0005418967,0.001051165,0.0008300134,0.0004248866,0.0005939555,0.0007368996,0.001087701,0.0008965278,0.001282828],"category_scores_gemma":[0.001314425,0.0005414441,0.000660539,0.000387002,0.0008306424,0.0007534896,0.001687543,0.00118752,0.0002605978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006602489,"about_ca_system_score_gemma":0.001657005,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0254567,"about_ca_topic_score_gemma":0.0174686,"domain_scores_codex":[0.9997299,0.00007162942,0.00000967772,0.00005804689,0.00008816797,0.00004250849],"domain_scores_gemma":[0.9995921,0.000186554,0.00005590202,0.00004492916,0.00007700534,0.0000435544],"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":[0.00003025211,0.00001559749,0.000221667,0.00001813292,0.00001395742,0.00003364628,0.0000436155,0.9816803,0.0006534792,0.002081807,0.0005990601,0.01460849],"study_design_scores_gemma":[0.000003766062,0.000005541615,0.00001861762,0.000001166991,0.000001110032,0.000002921558,0.000004788154,0.9987642,0.0001760209,0.0007880243,0.0002320865,0.000001761435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01772786,0.000123983,0.9785196,0.0001249544,0.00003860636,0.00003378018,0.00004150692,0.001438798,0.001950865],"genre_scores_gemma":[0.7471903,0.0001900256,0.2484741,0.0001168956,0.00004733404,0.0001790455,0.0002927466,0.0002920306,0.003217667],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0254567,"threshold_uncertainty_score":0.05061704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01260763696471445,"score_gpt":0.2427243174068686,"score_spread":0.2301166804421541,"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."}}