{"id":"W4285102511","doi":"10.1109/icra46639.2022.9811883","title":"Human Navigational Intent Inference with Probabilistic and Optimal Approaches","year":2022,"lang":"en","type":"article","venue":"2022 International Conference on Robotics and Automation (ICRA)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Inference; Probabilistic logic; Bayesian inference; Bayesian probability; Artificial intelligence; Function (biology); Heading (navigation); Machine learning; Prior probability; Statistical model; 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":[0.002710014,0.001089172,0.001559697,0.002100017,0.0007510909,0.001561148,0.002378492,0.001938717,0.002182349],"category_scores_gemma":[0.01318296,0.001446211,0.00144656,0.001259746,0.001700993,0.002511782,0.001592652,0.002089825,0.0003970652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001735605,"about_ca_system_score_gemma":0.002357836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02700366,"about_ca_topic_score_gemma":0.02211111,"domain_scores_codex":[0.9985994,0.0004824816,0.00008383761,0.0003890328,0.0003155786,0.0001297529],"domain_scores_gemma":[0.9945422,0.004146022,0.0004124417,0.000345743,0.0004281073,0.0001255072],"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":[0.0000483781,0.00003370357,0.00101099,0.00004471149,0.00005235351,0.0000515528,0.00008342596,0.9518681,0.0001836119,0.01887938,0.000546122,0.02719765],"study_design_scores_gemma":[0.000005967461,0.000007988758,0.0001359812,0.000008286216,0.000006006973,0.00001086358,0.000008569494,0.9755477,0.0000915586,0.02393834,0.0002303647,0.00000838869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0067494,0.0001945864,0.9915,0.0002088578,0.00002354753,0.00002998918,0.00006322209,0.0002122897,0.001018125],"genre_scores_gemma":[0.7021866,0.0005667327,0.2925878,0.0003366736,0.0001875548,0.0002292906,0.000491683,0.0001514708,0.003262199],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02700366,"threshold_uncertainty_score":0.05369294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03667728930411638,"score_gpt":0.2435174611707259,"score_spread":0.2068401718666095,"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."}}