{"id":"W3133575899","doi":"10.1109/tvt.2021.3062653","title":"Adaptive Computing Scheduling for Edge-Assisted Autonomous Driving","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Age of Information Optimization","field":"Computer Science","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Edge computing; Scheduling (production processes); Distributed computing; Job shop scheduling; Reinforcement learning; Real-time computing; Obstacle; Latency (audio); Dynamic priority scheduling; Enhanced Data Rates for GSM Evolution; Embedded system; Computer network; Mathematical optimization; Artificial intelligence; Quality of service","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006231281,0.0004633966,0.0005168055,0.0002632976,0.0004601848,0.0005695468,0.0009407489,0.0003764661,0.001346344],"category_scores_gemma":[0.001787741,0.000208812,0.0001826884,0.0002360586,0.0004992309,0.0006622093,0.0006875107,0.0006231893,0.0001946202],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008160037,"about_ca_system_score_gemma":0.001063903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00508117,"about_ca_topic_score_gemma":0.004987182,"domain_scores_codex":[0.9996572,0.00007645859,0.00001616455,0.00008877611,0.0000643237,0.00009699899],"domain_scores_gemma":[0.9993113,0.0002618186,0.0001116851,0.00006244744,0.000142833,0.0001098808],"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":[0.0001736328,0.00008568406,0.0007139701,0.00002599014,0.00001268088,0.00004690668,0.00005109212,0.9575592,0.003153779,0.004876277,0.0006143126,0.03268649],"study_design_scores_gemma":[0.000003006603,0.00001616423,0.00005864468,7.017042e-7,0.000001127387,0.000003006589,0.000004820247,0.998744,0.0002369892,0.0008298177,0.0001002095,0.000001466561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1199534,0.000236189,0.8748959,0.0002513123,0.00007026044,0.00006219846,0.00005488937,0.0004439877,0.004031785],"genre_scores_gemma":[0.9819342,0.00004072649,0.0170404,0.00003691964,0.00001162036,0.00001878398,0.00002402572,0.00001765838,0.0008756984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00508117,"threshold_uncertainty_score":0.01010323,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01446231607591557,"score_gpt":0.2393482040029253,"score_spread":0.2248858879270098,"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."}}