{"id":"W4391164477","doi":"10.1109/twc.2024.3354507","title":"Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Unicast; Reinforcement learning; Situation awareness; Benchmark (surveying); Perception; Resource allocation; Inefficiency; Distributed computing; Channel (broadcasting); Scheme (mathematics); Computer network; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006780528,0.0007138318,0.0008181974,0.0002666642,0.0003996308,0.0006002806,0.001193555,0.0006708286,0.0008782792],"category_scores_gemma":[0.001781996,0.0003432396,0.00039714,0.0002517699,0.0006373057,0.0006233102,0.001147692,0.001059525,0.0001530368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006810627,"about_ca_system_score_gemma":0.001040928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007892208,"about_ca_topic_score_gemma":0.006058061,"domain_scores_codex":[0.999693,0.00007964388,0.00001107919,0.00007568152,0.00007218972,0.00006838305],"domain_scores_gemma":[0.9992131,0.0003876516,0.0001270869,0.00004846396,0.0001621621,0.00006151641],"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":[0.00002429488,0.00002536651,0.0003303985,0.00001460517,0.00001244328,0.00002842855,0.00003432373,0.9867929,0.0006983831,0.001516881,0.0002063714,0.01031559],"study_design_scores_gemma":[0.00000225653,0.0000111359,0.00002205392,6.041827e-7,0.000001197821,0.000002046568,0.000003085959,0.9994587,0.00005693646,0.0004027117,0.00003822941,9.555463e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0449968,0.0001597318,0.9522089,0.0001735879,0.00003115781,0.00003316969,0.00001687471,0.00025282,0.00212687],"genre_scores_gemma":[0.9763163,0.00005284812,0.022562,0.00005546618,0.00001431203,0.00006094394,0.00002538284,0.00001496297,0.0008978151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007892208,"threshold_uncertainty_score":0.01569259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03389261189413471,"score_gpt":0.264247795764669,"score_spread":0.2303551838705343,"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."}}