{"id":"W4415782431","doi":"10.1177/02783649251365282","title":"Learning an interpretable logic monitor for risk-aware and socially-compliant trajectory planning","year":2025,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Toyota Research Institute, North America","keywords":"Leverage (statistics); Task (project management); Parameterized complexity; Trajectory; Software deployment; Robot; Control (management); Plan (archaeology)","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":[0.001234765,0.001096445,0.0005647913,0.0007325142,0.000375588,0.001320246,0.001556152,0.0009215153,0.002052878],"category_scores_gemma":[0.008670324,0.0005850259,0.0007091268,0.0003096699,0.001207979,0.002016689,0.001782391,0.001870558,0.0003406686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001550239,"about_ca_system_score_gemma":0.002180838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005470359,"about_ca_topic_score_gemma":0.0113145,"domain_scores_codex":[0.9991245,0.0002160561,0.00004810062,0.0002979382,0.0002412982,0.00007211303],"domain_scores_gemma":[0.9976526,0.001278737,0.0003284029,0.0002661606,0.0003346732,0.0001395232],"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":[0.0002209671,0.0001574543,0.005216418,0.0001270372,0.00006247631,0.0002682621,0.0002862741,0.8392617,0.006081562,0.02484078,0.002905219,0.1205719],"study_design_scores_gemma":[0.000008616853,0.00002369127,0.0001155937,0.000007020165,0.000006166603,0.00001776414,0.00001357369,0.9880521,0.0009473907,0.01040048,0.0004034457,0.000004213575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03972515,0.0001163895,0.9554441,0.0003790387,0.00002644886,0.0000851982,0.0002744499,0.002303278,0.001645913],"genre_scores_gemma":[0.7412309,0.0001215063,0.2555495,0.0002304005,0.0000327465,0.0001982458,0.0008214272,0.0002342887,0.001580864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005470359,"threshold_uncertainty_score":0.01124787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05053092852164519,"score_gpt":0.3791718383832804,"score_spread":0.3286409098616352,"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."}}