{"id":"W4252868466","doi":"10.3901/jme.2021.22.237","title":"Research on Deep Reinforcement Learning-based Intelligent Car-following Control and Energy Management Strategy for Hybrid Electric Vehicles","year":2021,"lang":"en","type":"article","venue":"Journal of Mechanical Engineering","topic":"Electric and Hybrid Vehicle Technologies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Reinforcement learning; Energy management; Control (management); Computer science; Automotive engineering; Reinforcement; Control engineering; Engineering; Artificial intelligence; Energy (signal processing)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009187795,0.0003575503,0.0004220321,0.000403451,0.0006164176,0.001424525,0.0007492293,0.0007903004,0.003212562],"category_scores_gemma":[0.002035948,0.0001891176,0.0003834197,0.0004721242,0.0009789556,0.002740053,0.0007261951,0.0009764327,0.0002794514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001554072,"about_ca_system_score_gemma":0.002285627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01127157,"about_ca_topic_score_gemma":0.006920358,"domain_scores_codex":[0.9995547,0.0001364294,0.00002704036,0.0001147546,0.0001051435,0.00006196037],"domain_scores_gemma":[0.9995815,0.0001824421,0.00005267896,0.00002036793,0.0001270377,0.00003601624],"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":[0.0001924387,0.0001751846,0.008873612,0.0005629826,0.0001615177,0.0002400986,0.001146727,0.419627,0.00466147,0.3232309,0.007751556,0.2333765],"study_design_scores_gemma":[0.00005226223,0.0001596283,0.003608588,0.0001184652,0.00007368216,0.00009276933,0.0007771492,0.8712006,0.002674989,0.1009399,0.02025,0.00005186007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2059747,0.009678493,0.6363519,0.009477021,0.0005676073,0.0001487644,0.0001915192,0.000436045,0.137174],"genre_scores_gemma":[0.9640028,0.002230929,0.02263964,0.000224864,0.00006992705,0.00005374649,0.00006010133,0.00001911739,0.01069889],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01127157,"threshold_uncertainty_score":0.02241194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01948515240296394,"score_gpt":0.2591826360482493,"score_spread":0.2396974836452853,"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."}}