{"id":"W4387883791","doi":"10.1109/icc45041.2023.10279056","title":"Platoon Leader Selection, User Association and Resource Allocation on a C-V2X Based Highway: A Reinforcement Learning Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Traffic control and management","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Platoon; Reinforcement learning; Selection (genetic algorithm); Computer science; Association (psychology); Resource allocation; Reinforcement; Resource (disambiguation); Resource management (computing); Transport engineering; Artificial intelligence; Engineering; Computer network; Control (management); Psychology; Structural engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0002670501,0.0001025131,0.00009104548,0.0001478822,0.00008474904,0.00004922675,0.00003454024,0.00006103922,0.00002658454],"category_scores_gemma":[0.00002703607,0.0001001667,0.00002400204,0.0002684904,0.000003627236,0.00006498084,0.0000120464,0.0001267737,0.00006255737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001762646,"about_ca_system_score_gemma":0.000006397819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001623894,"about_ca_topic_score_gemma":0.00001569836,"domain_scores_codex":[0.9992892,0.00002212373,0.000135772,0.0001540227,0.000201077,0.0001977783],"domain_scores_gemma":[0.9997937,0.00005161387,0.00002757852,0.00007054467,0.00002089618,0.000035636],"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":[0.00001002898,0.000008731751,0.000224942,0.00003937531,0.00004472289,2.71406e-7,0.0001974359,0.9723154,0.0002951528,0.001159181,0.02143776,0.004267035],"study_design_scores_gemma":[0.0005628046,0.00004261404,0.006013757,0.000009899757,0.00001715053,1.641214e-7,0.0001538832,0.866232,0.0001645768,0.000003962163,0.1266863,0.0001128594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3710197,0.0001086038,0.4445458,0.006337029,0.0005847845,0.002346216,0.000002606316,0.01463973,0.1604155],"genre_scores_gemma":[0.9813626,0.00001039531,0.0002097122,0.0001580837,0.00005630176,0.00007379973,0.00005022705,0.00002103031,0.0180579],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6103429,"threshold_uncertainty_score":0.408468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009830596881387528,"score_gpt":0.1858465739852092,"score_spread":0.1760159771038217,"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."}}