{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005136252,0.001118731,0.0007323753,0.0004751232,0.0005617839,0.001904494,0.003763633,0.001672763,0.006094826],"category_scores_gemma":[0.00176415,0.000762465,0.001581557,0.000538988,0.0008069255,0.002608514,0.002583391,0.002154377,0.002617665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007409899,"about_ca_system_score_gemma":0.002013381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01775888,"about_ca_topic_score_gemma":0.0192257,"domain_scores_codex":[0.9997026,0.00004354567,0.0000189224,0.00008719818,0.0001165135,0.00003106586],"domain_scores_gemma":[0.9996798,0.00008343424,0.00003339737,0.00008353815,0.00007515655,0.000044678],"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":[0.0002402486,0.00009396992,0.0009057425,0.0002010725,0.000115087,0.000208046,0.0002324257,0.8339881,0.004713845,0.04132001,0.01490956,0.103072],"study_design_scores_gemma":[0.00001426279,0.00002624816,0.00008551835,0.00001066825,0.00001522477,0.00002521715,0.00001107373,0.9786364,0.0009651284,0.01322656,0.006971034,0.00001262707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002501404,0.00009614399,0.9884428,0.0001641422,0.00004271314,0.00007987358,0.0006751456,0.005760465,0.002237201],"genre_scores_gemma":[0.2118389,0.0006049654,0.7731471,0.0002727609,0.00006739753,0.0009248828,0.003223116,0.001315876,0.008605052],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01775888,"threshold_uncertainty_score":0.03531104,"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."}}