{"id":"W4407168874","doi":"10.1109/lwc.2025.3539638","title":"Hybrid LLM-DDQN-Based Joint Optimization of V2I Communication and Autonomous Driving","year":2025,"lang":"en","type":"article","venue":"IEEE Wireless Communications Letters","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; York University","funders":"","keywords":"Computer science; Joint (building); Computer network; Engineering","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":[0.0003147785,0.0002005682,0.0002954788,0.0002698314,0.000237993,0.00006391415,0.0008420247,0.00007777075,0.000008093352],"category_scores_gemma":[0.00002289924,0.0002458675,0.00007552924,0.000330366,0.0002863842,0.0001762029,0.0001673796,0.0003654337,0.000003743479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001721771,"about_ca_system_score_gemma":0.00004518081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005133022,"about_ca_topic_score_gemma":0.0000634032,"domain_scores_codex":[0.9987465,0.0002042064,0.0005086996,0.0001746605,0.0001281796,0.0002377983],"domain_scores_gemma":[0.9971414,0.0002929224,0.0001274828,0.002294133,0.00008652579,0.000057523],"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":[0.000002357957,0.00005262519,0.0003263494,0.00006875117,0.0000644306,5.450729e-7,0.0001008055,0.9807993,0.0123159,0.0004622833,0.001460201,0.004346446],"study_design_scores_gemma":[0.0003774971,0.000007915236,0.0009314592,0.0002885943,0.00005032723,0.000003150889,0.00002760411,0.9889417,0.008127113,0.00003589811,0.00101017,0.00019854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5059481,0.001698951,0.4845167,0.00524087,0.0001794251,0.0004875088,0.00001468732,0.0004975759,0.001416278],"genre_scores_gemma":[0.964618,0.0007311917,0.03389354,0.000471299,0.00001221588,0.00009251038,0.0001242333,0.00004229845,0.00001466026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.45867,"threshold_uncertainty_score":0.9999993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009442303099471087,"score_gpt":0.2140161326704723,"score_spread":0.2045738295710012,"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."}}