{"id":"W4396909936","doi":"10.1109/tiv.2024.3401051","title":"Bayesian Fault Injection Safety Testing for Highly Automated Vehicles With Uncertainty","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministry of Education and Child Care","funders":"National Natural Science Foundation of China","keywords":"Monte Carlo method; Computer science; Fault (geology); Bayesian probability; Reliability engineering; Collision; Bayesian network; Dynamic Bayesian network; Reliability (semiconductor); Software deployment; Simulation; Data mining; Engineering; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00161483,0.0003679567,0.000462201,0.0008951121,0.0008545532,0.0006381303,0.0005151675,0.0001957878,0.0001732692],"category_scores_gemma":[0.0001714659,0.0002563552,0.0004245064,0.002794404,0.0001942729,0.0004956687,0.000003299403,0.0004108565,0.0003440181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002378411,"about_ca_system_score_gemma":0.0002037828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004483562,"about_ca_topic_score_gemma":0.0008690783,"domain_scores_codex":[0.9961237,0.0002334408,0.001039624,0.0009726619,0.001151867,0.0004786665],"domain_scores_gemma":[0.9945403,0.003926235,0.0001619121,0.000584642,0.0005865335,0.0002003324],"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":[0.0004331518,0.0001155691,0.00009268399,0.00002379903,0.0001859859,0.00001054573,0.0005304895,0.5537703,0.001125387,0.0002181242,0.0007932356,0.4427007],"study_design_scores_gemma":[0.0002844818,0.0008263919,0.0002407872,0.0002061603,0.0002192629,0.0000394377,0.001448541,0.9569658,0.0235937,0.006091274,0.00967269,0.0004114985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05183029,0.0001645346,0.9430534,0.001763861,0.0008073741,0.0004801533,0.0001965683,0.001210477,0.0004933383],"genre_scores_gemma":[0.992309,0.0001175464,0.005215358,0.000141802,0.0001212654,0.0001101697,0.00001069877,0.0000515946,0.001922571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9404787,"threshold_uncertainty_score":0.9999889,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06249454754738269,"score_gpt":0.3446773087093376,"score_spread":0.2821827611619549,"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."}}