{"id":"W4402187256","doi":"10.1109/tvt.2024.3454574","title":"Hierarchical Transformers for Motion Forecasting Based on Inverse Reinforcement Learning","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Reinforcement learning; Transformer; Artificial intelligence; Computer science; Engineering; Machine learning; Electrical engineering; Voltage","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001599563,0.00016368,0.0001337674,0.0004938961,0.0003641483,0.00007512661,0.0002742408,0.000191902,0.00001216605],"category_scores_gemma":[0.000005403983,0.0001520729,0.0001676342,0.0008728349,0.00007441733,0.000144837,0.000001835144,0.0006152509,0.00002952898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008394254,"about_ca_system_score_gemma":0.00004234705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003166968,"about_ca_topic_score_gemma":0.000006623963,"domain_scores_codex":[0.9988059,0.00002444177,0.0002208741,0.0004610645,0.0001703575,0.0003173498],"domain_scores_gemma":[0.9994795,0.0001453837,0.00002989069,0.0002450166,0.00003776005,0.00006247223],"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":[0.00001124519,0.00004523407,6.33039e-7,0.00002483984,0.00001740144,0.000008107663,0.0000259494,0.6150988,0.001672953,0.009782056,0.00005794243,0.3732548],"study_design_scores_gemma":[0.0002858105,0.000443607,8.014647e-7,0.00007349344,0.00001959977,0.00001493363,0.00001389417,0.9641171,0.02413459,0.00156154,0.009186056,0.000148566],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004181977,0.00001611757,0.9866827,0.007184956,0.0003144861,0.0004983525,0.00000327251,0.0009353759,0.0001828153],"genre_scores_gemma":[0.9874355,0.00001627574,0.01157794,0.0002984526,0.00002980311,0.0004519807,0.000005291775,0.00002005182,0.0001647463],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9832535,"threshold_uncertainty_score":0.6201354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02042884831216716,"score_gpt":0.2465734333635621,"score_spread":0.2261445850513949,"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."}}