{"id":"W4312424509","doi":"10.1109/iros47612.2022.9981421","title":"Vehicle Type Specific Waypoint Generation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Waypoint; Probabilistic logic; Computer science; Reinforcement learning; Function (biology); Artificial intelligence; Machine learning; Real-time computing","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003181855,0.0002225801,0.0002370034,0.0002098497,0.0002922752,0.0001104396,0.0003612478,0.0001043541,0.001772468],"category_scores_gemma":[0.000009492183,0.0002276886,0.00006090002,0.0001600142,0.00006082176,0.0001027206,0.0001034179,0.0005067102,0.0001805262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000299911,"about_ca_system_score_gemma":0.00003120847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004231633,"about_ca_topic_score_gemma":0.00001331359,"domain_scores_codex":[0.9984603,0.00007158532,0.0004487796,0.0003637285,0.0004061992,0.0002494317],"domain_scores_gemma":[0.9994341,0.00003158128,0.00008929838,0.0002678578,0.00009793512,0.00007924821],"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":[0.0001223361,0.0002079664,0.001077047,0.00005402041,0.0002980288,0.00008073218,0.001073386,0.429285,0.106739,0.4160735,0.02492252,0.02006642],"study_design_scores_gemma":[0.0003280019,0.0002733998,0.0003925277,0.00003609222,0.00001256606,0.00008165491,0.0009845435,0.9298811,0.00790111,0.0007449327,0.05891916,0.0004449293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.923548,0.002187677,0.02455794,0.00122461,0.01666855,0.000852189,0.0001764539,0.0008438029,0.02994079],"genre_scores_gemma":[0.9955685,0.0009566444,0.00004479195,0.00009482112,0.000303052,0.0001160189,0.0001001407,0.00003246248,0.00278356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.500596,"threshold_uncertainty_score":0.99914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06451494096416267,"score_gpt":0.2553018640587518,"score_spread":0.1907869230945891,"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."}}