{"id":"W4411476086","doi":"10.1007/s10489-025-06665-1","title":"MSTD: A multimodal vehicle trajectory prediction method based on multi-level spatiotemporal decoupling","year":2025,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Decoupling (probability); Trajectory; Artificial intelligence; Real-time computing; Computer vision; Control 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":[0.0003230417,0.0002338892,0.0002233632,0.0002338845,0.0001433368,0.00001656262,0.0002809926,0.0003123873,0.00005857568],"category_scores_gemma":[0.000033245,0.0002534492,0.00006940107,0.0003646705,0.00007469508,0.00005743563,0.00003243657,0.0005116535,0.00009972871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001675093,"about_ca_system_score_gemma":0.00005424513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002425547,"about_ca_topic_score_gemma":0.00002618887,"domain_scores_codex":[0.9988117,0.00001835401,0.0003674853,0.0003639732,0.0001235608,0.0003149421],"domain_scores_gemma":[0.9992842,0.0001998207,0.00004078837,0.0003927119,0.00003115684,0.00005130755],"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":[0.00005699282,0.00009426527,0.000957455,0.00005248153,0.00004264575,0.000002896681,0.0001213545,0.6875792,0.007714754,0.004286408,0.00009215785,0.2989994],"study_design_scores_gemma":[0.0002187876,0.00002933614,0.007038689,0.00003353582,0.00001495685,5.016155e-7,0.00006842201,0.8215608,0.1700948,0.0004886736,0.0002822364,0.0001692208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04001094,0.00006427081,0.9531872,0.00005974311,0.0002688935,0.0003468723,0.00002287382,0.001186607,0.00485266],"genre_scores_gemma":[0.9092704,0.00001633494,0.09027926,0.0001728912,0.00002719474,0.00011274,0.00001558433,0.00002900974,0.00007660307],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8692595,"threshold_uncertainty_score":0.9999918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364105327654182,"score_gpt":0.2733539628554051,"score_spread":0.2497129095788633,"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."}}