{"id":"W2995454304","doi":"10.1109/tvt.2019.2958622","title":"Deep Learning-Based Driving Maneuver Prediction System","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Advanced driver assistance systems; Driving simulator; Vehicle dynamics; Set (abstract data type); Fuse (electrical); Computer science; Engineering; Artificial neural network; Work (physics); Safe driving; Simulation; Artificial intelligence; Control engineering; Automotive 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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001254388,0.0002665744,0.0002994622,0.0006888972,0.0001989343,0.00001291392,0.0002768586,0.0008987379,0.0001217647],"category_scores_gemma":[0.000002934151,0.0002908754,0.0001301212,0.0005862315,0.0001105182,0.00009685112,0.000002013355,0.001246444,0.0008774011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002894232,"about_ca_system_score_gemma":0.00001830341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003097685,"about_ca_topic_score_gemma":0.00001454106,"domain_scores_codex":[0.9987166,0.00003487756,0.0003068121,0.0003704306,0.0001542894,0.0004169822],"domain_scores_gemma":[0.9992649,0.00004230845,0.00004694932,0.0005530805,0.00004264563,0.00005009395],"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":[0.00001165688,0.00004691451,0.0008723809,0.00008378788,0.00009541588,0.00002518797,0.00002027894,0.9733809,0.008727649,0.0004955066,0.00001111927,0.01622922],"study_design_scores_gemma":[0.0006530359,0.000224396,0.0003824976,0.00008139852,0.00005592211,0.00008015992,0.0001968846,0.9036875,0.09171616,0.0000499571,0.002581664,0.0002904357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3893423,0.00008249571,0.6019977,0.0001272854,0.0006242743,0.0002846487,0.000003958925,0.006920638,0.0006166162],"genre_scores_gemma":[0.9989724,0.0000245209,0.0005844633,0.00002189578,0.00001566935,0.0001184464,0.000004809961,0.00007035379,0.0001874248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.60963,"threshold_uncertainty_score":0.9999543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002815007703812615,"score_gpt":0.1685892621526575,"score_spread":0.1657742544488449,"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."}}