{"id":"W3125926538","doi":"10.1109/cvpr46437.2021.01417","title":"MP3: A Unified Model to Map, Perceive, Predict and Plan","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Intersection (aeronautics); Scale (ratio); Key (lock); Artificial intelligence; SAFER; Plan (archaeology); Term (time); Robot; Planner; Motion (physics); Advanced driver assistance systems; Component (thermodynamics); Bridge (graph theory); Semantic mapping; Machine learning; Data mining; Computer security; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007504682,0.0002456974,0.000307986,0.0001125585,0.00004280522,0.00003569367,0.0002277465,0.0006300844,0.00008107459],"category_scores_gemma":[0.000008544393,0.0002552908,0.00005035846,0.00005270791,0.00003604127,0.00003469006,0.0005308138,0.0007269059,0.00002597202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006115909,"about_ca_system_score_gemma":0.00005429932,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002884618,"about_ca_topic_score_gemma":0.00007094491,"domain_scores_codex":[0.9991058,0.000009917531,0.0002033165,0.000361139,0.00007871975,0.000241162],"domain_scores_gemma":[0.9993724,0.00001714289,0.00001480532,0.0004692288,0.00002304055,0.0001033311],"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":[0.00001780926,0.00002657858,0.0008075217,0.0004368504,0.000234214,0.00005702457,0.001834864,0.9800252,0.0009266071,0.00368772,0.004852752,0.0070928],"study_design_scores_gemma":[0.0001624469,0.00001668197,0.003178932,0.00008922592,0.00003388371,0.00001285111,0.0002562377,0.9927242,0.000549136,0.001757606,0.0008130067,0.0004057561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8183034,0.0004937146,0.1490067,0.0007360719,0.0003338019,0.0004419484,0.00008701233,0.002685846,0.02791149],"genre_scores_gemma":[0.9839105,0.0001783624,0.01410085,0.0001362911,0.00003237514,0.00005170993,0.00006071304,0.00004062249,0.001488555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1656071,"threshold_uncertainty_score":0.9999899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01567825395237735,"score_gpt":0.2090059808122378,"score_spread":0.1933277268598605,"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."}}