{"id":"W3091006519","doi":"10.1613/jair.1.12531","title":"MADRaS : Multi Agent Driving Simulator","year":2021,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Research","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada); University of Alberta","funders":"Indian Institute of Technology Kharagpur; Eidgenössische Technische Hochschule Zürich","keywords":"Reinforcement learning; Computer science; Variety (cybernetics); Simulation; Track (disk drive); Interface (matter); Driving simulator; Generalization; Artificial intelligence; Operating system","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":[0.0003517981,0.0009189993,0.0005238865,0.000377181,0.0002740478,0.0005578734,0.001981911,0.001008414,0.01020387],"category_scores_gemma":[0.001273465,0.0004056266,0.0006719326,0.0002342354,0.000289517,0.0005715973,0.0009897075,0.001312068,0.00173619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004278388,"about_ca_system_score_gemma":0.001091972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009364827,"about_ca_topic_score_gemma":0.009874087,"domain_scores_codex":[0.9998337,0.00004079768,0.00001457966,0.00002607072,0.00005803764,0.00002675916],"domain_scores_gemma":[0.9996252,0.0001725394,0.00002773577,0.00003273245,0.0000860041,0.0000557314],"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":[0.000364278,0.0002430276,0.00261364,0.0004416638,0.0001205143,0.0002969625,0.0001837906,0.919385,0.007471755,0.008092735,0.03119911,0.0295876],"study_design_scores_gemma":[0.0001536999,0.00006985058,0.0003645096,0.00001415311,0.00001591796,0.00003816241,0.00001876786,0.975646,0.001962879,0.00207643,0.0196208,0.00001893476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1782802,0.001119493,0.6460738,0.001329442,0.0007098787,0.002115611,0.02354344,0.09202448,0.05480365],"genre_scores_gemma":[0.7270969,0.0006579609,0.2300697,0.0003663127,0.00004912961,0.00227749,0.02011427,0.002720498,0.01664773],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01020387,"threshold_uncertainty_score":0.03413534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1355636750621388,"score_gpt":0.3935038671666261,"score_spread":0.2579401921044873,"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."}}