{"id":"W4391691216","doi":"10.1049/itr2.12489","title":"Joint resource allocation and security redundancy for autonomous driving based on deep reinforcement learning algorithm","year":2024,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"IoT and Edge/Fog Computing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Reinforcement learning; Computer science; Edge computing; Redundancy (engineering); Server; Resource allocation; Distributed computing; Enhanced Data Rates for GSM Evolution; Computer network; Artificial intelligence","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":[0.001137465,0.0002739629,0.0003013461,0.0002463626,0.000306233,0.0003580617,0.0003275605,0.0001170195,0.000002656533],"category_scores_gemma":[0.00001912056,0.0002636823,0.000159082,0.0002859566,0.0000322598,0.0002206296,0.0000336325,0.0003014803,0.00002018232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001922043,"about_ca_system_score_gemma":0.0001044801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006676085,"about_ca_topic_score_gemma":0.000003628744,"domain_scores_codex":[0.9977481,0.00007039496,0.0006692186,0.0006844357,0.0003778434,0.0004500144],"domain_scores_gemma":[0.9990929,0.0001886969,0.0001152604,0.0003669088,0.00008830908,0.0001479298],"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":[0.00004747061,0.0002095999,0.0008610358,0.003374476,0.0002615294,0.000181314,0.03225787,0.5716762,0.0004969972,0.02260113,0.003116915,0.3649155],"study_design_scores_gemma":[0.0001225376,0.0002443314,0.00006508047,0.0007830099,0.00002013002,0.0000171563,0.0001099288,0.890573,0.001075728,0.0001300068,0.1065899,0.0002692257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0027277,0.0008218039,0.9878988,0.0003331612,0.005795964,0.0007609911,6.495421e-7,0.0005787573,0.001082189],"genre_scores_gemma":[0.9933671,0.00002362284,0.004755239,0.00007757335,0.001061156,0.00009727152,0.00005793732,0.00003698955,0.0005230525],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9906394,"threshold_uncertainty_score":0.9999815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01826318344437355,"score_gpt":0.240304182016304,"score_spread":0.2220409985719305,"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."}}