{"id":"W2897053701","doi":"10.1109/tiv.2018.2874555","title":"Estimation of Steering Angle and Collision Avoidance for Automated Driving Using Deep Mixture of Experts","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Artificial intelligence; Robustness (evolution); Computer science; Particle filter; Obstacle avoidance; Parametric statistics; Computer vision; Convolutional neural network; Monocular; Pattern recognition (psychology); Kalman filter; Mathematics; Mobile robot; Robot; Statistics","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":[0.0005001344,0.0008830749,0.0007500912,0.0006503292,0.0002951583,0.0004549894,0.0009716747,0.0007560059,0.0007223722],"category_scores_gemma":[0.001312059,0.0006371505,0.0007232464,0.0004232373,0.0003161218,0.0007068965,0.0006632392,0.0009060234,0.0002969225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004239668,"about_ca_system_score_gemma":0.001031803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009615785,"about_ca_topic_score_gemma":0.00978822,"domain_scores_codex":[0.9997002,0.00004236518,0.000013076,0.00009207613,0.00009607376,0.00005628984],"domain_scores_gemma":[0.9995888,0.0001396815,0.00006033682,0.00004043551,0.0001361081,0.00003475741],"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":[0.0001710178,0.00007542413,0.002120953,0.00004852923,0.00008334061,0.00007014263,0.000109316,0.7717913,0.01129127,0.002719837,0.001006929,0.2105118],"study_design_scores_gemma":[0.000001971453,0.000009804367,0.0001959834,0.000001447629,0.000002888984,0.00001117958,0.0000030683,0.9983494,0.0008261717,0.0004546602,0.0001393336,0.000004044301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02145572,0.0001112064,0.9774936,0.00003698665,0.00001828267,0.00001164154,0.00002444253,0.0003279692,0.0005200943],"genre_scores_gemma":[0.7637011,0.0001689665,0.2332552,0.00008229761,0.00004472549,0.00004966142,0.0002445951,0.00007379009,0.002379659],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009615785,"threshold_uncertainty_score":0.01911962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01368145576668912,"score_gpt":0.2553113560878842,"score_spread":0.2416299003211951,"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."}}