{"id":"W4387030822","doi":"10.48550/arxiv.2309.12508","title":"A Diffusion-Model of Joint Interactive Navigation","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; Alliance de recherche numérique du Canada; Compute Canada; Lawrence Berkeley National Laboratory; Canadian Institute for Advanced Research; U.S. Department of Energy","keywords":"Computer science; Trajectory; Sampling (signal processing); Variety (cybernetics); Set (abstract data type); State (computer science); Class (philosophy); Scale (ratio); Joint (building); Real-time computing; Data mining; Simulation; Artificial intelligence; Machine learning; Distributed computing; Algorithm; Engineering; Computer vision","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001766128,0.0008494201,0.001059199,0.000846175,0.0007317279,0.001248111,0.002696247,0.001741702,0.004662362],"category_scores_gemma":[0.005447869,0.0006277607,0.001095329,0.001011149,0.001588609,0.002218036,0.001902383,0.002213743,0.0008203005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001738033,"about_ca_system_score_gemma":0.001115371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01923373,"about_ca_topic_score_gemma":0.01429974,"domain_scores_codex":[0.9990408,0.0003051597,0.00004557143,0.0003247608,0.0001709,0.0001127498],"domain_scores_gemma":[0.9977733,0.001335826,0.0002337343,0.0002219707,0.0002718874,0.0001631849],"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":[0.00008395493,0.00003091868,0.001245785,0.00003323113,0.00002904544,0.00008811695,0.0001497184,0.9051363,0.0008834662,0.08202929,0.001388425,0.008901716],"study_design_scores_gemma":[0.000007858572,0.00001044378,0.00008527783,0.000002260947,0.000003788974,0.00001521626,0.000006137832,0.9882918,0.0001176411,0.01089583,0.0005578951,0.000005871659],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03018634,0.0002273746,0.9643158,0.0005616181,0.00007669879,0.00004992813,0.000507416,0.0004289744,0.00364591],"genre_scores_gemma":[0.8902957,0.0004526725,0.09136309,0.0001896817,0.000123455,0.0002508722,0.0009570557,0.0001575112,0.01620995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01923373,"threshold_uncertainty_score":0.03824353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07547138333705651,"score_gpt":0.1834450761420718,"score_spread":0.1079736928050153,"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."}}