{"id":"W4400351292","doi":"10.1109/access.2024.3423809","title":"Deep Learning-Based Motion Prediction Leveraging Autonomous Driving Datasets: State-of-the-Art","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Motion (physics); State (computer science); Deep learning; Machine learning; Algorithm","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":[0.001077479,0.002924345,0.001253372,0.002076897,0.0006249389,0.00140691,0.002892204,0.001783406,0.002592162],"category_scores_gemma":[0.004841328,0.0005880155,0.001264778,0.002339032,0.0005725949,0.002389636,0.001352065,0.002473379,0.00281082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055321,"about_ca_system_score_gemma":0.001607369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03559745,"about_ca_topic_score_gemma":0.04081722,"domain_scores_codex":[0.9990163,0.0001576185,0.00006887412,0.0003616229,0.0002668235,0.0001288647],"domain_scores_gemma":[0.9986413,0.000362884,0.00009582211,0.000370007,0.0004221721,0.0001077714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004819047,0.001329671,0.0200306,0.001032172,0.000509468,0.0001660822,0.0001077613,0.2184515,0.003587916,0.002475288,0.1171602,0.6346673],"study_design_scores_gemma":[0.00003502292,0.0001640284,0.005946621,0.0001991382,0.00008308551,0.00009042636,0.0001051548,0.9646275,0.00459493,0.003686165,0.02040287,0.00006508881],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.4153216,0.04046178,0.301231,0.005188527,0.00473184,0.001039278,0.1539637,0.04916519,0.02889702],"genre_scores_gemma":[0.5421387,0.007678884,0.1151819,0.0007900337,0.0006167441,0.000474049,0.3202706,0.0006607476,0.01218839],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.03559745,"threshold_uncertainty_score":0.07078052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.011165202745046,"score_gpt":0.2417143132927873,"score_spread":0.2305491105477412,"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."}}