{"id":"W4403967835","doi":"10.1155/2024/2179275","title":"Longitudinal Hierarchical Control of Autonomous Vehicle Based on Deep Reinforcement Learning and PID Algorithm","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Elevator Systems and Control","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"PID controller; Reinforcement learning; Computer science; Control (management); Reinforcement; Artificial intelligence; Algorithm; Control engineering; Control theory (sociology); Engineering; Temperature control; Structural engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001573306,0.0000950925,0.0002139548,0.0001195702,0.00002562809,0.00001521089,0.00003430812,0.000039771,0.00001345749],"category_scores_gemma":[0.000007045468,0.00008342588,0.00008313998,0.00008262841,0.00001653536,0.0001552854,3.372769e-7,0.0002439483,7.494036e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004026023,"about_ca_system_score_gemma":0.00002468386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001832294,"about_ca_topic_score_gemma":0.000003295163,"domain_scores_codex":[0.9991547,0.00001478819,0.0004442302,0.0000749255,0.0002038895,0.0001074293],"domain_scores_gemma":[0.9996606,0.0000887045,0.00008832318,0.00004006719,0.0000614739,0.00006086478],"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":[0.00008747367,0.00001083066,0.0007014853,0.0001497068,0.00006452881,0.000052173,0.0002735766,0.9182314,0.01053222,0.0002684574,0.000002174294,0.06962594],"study_design_scores_gemma":[0.002027298,0.0006795161,0.02149625,0.0003707935,0.00008549119,0.00001044062,0.00009010454,0.971865,0.002037361,0.00007904346,0.001144001,0.0001146883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2734984,0.001765547,0.7240549,0.00007225492,0.0003619624,0.0001181539,0.000004592317,0.00004545381,0.00007872179],"genre_scores_gemma":[0.9981032,0.00006043013,0.001694875,0.00001039841,0.00009408823,0.000004516141,0.000004433921,0.00001676502,0.00001126431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7246048,"threshold_uncertainty_score":0.3402009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003995683789664146,"score_gpt":0.2144864959312352,"score_spread":0.2104908121415711,"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."}}