{"id":"W2904140126","doi":"10.1609/aaai.v33i01.3301978","title":"Crash to Not Crash: Learn to Identify Dangerous Vehicles Using a Simulator","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Computer science; Crash; Classifier (UML); Driving simulator; Collision; Generator (circuit theory); Artificial intelligence; Simulation; Machine learning; Computer security","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003357778,0.0002661206,0.0003422564,0.0002077252,0.0001297027,0.00009572026,0.001006545,0.0001973107,0.0001472077],"category_scores_gemma":[0.0002306425,0.0002352136,0.0001035952,0.0006009845,0.0001074979,0.000179297,0.0002772833,0.0004220987,0.001017663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001173608,"about_ca_system_score_gemma":0.00004224098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002850122,"about_ca_topic_score_gemma":0.00001405511,"domain_scores_codex":[0.9982724,0.000008059749,0.0005039359,0.0004102864,0.0003146238,0.0004907642],"domain_scores_gemma":[0.9991084,0.00006551731,0.0001083699,0.0003065166,0.0002752602,0.0001359329],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008679611,0.00005627888,0.002072022,0.00009494481,0.00003622422,7.837513e-7,0.001404552,0.03580067,0.8770632,0.04478036,0.00009258707,0.03851157],"study_design_scores_gemma":[0.0000317556,0.0001541285,0.001627439,0.0002429711,0.00002411371,0.000003087901,0.0007645973,0.1061286,0.8845003,0.005757319,0.0004053171,0.0003604635],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919341,0.000009201681,0.003732287,0.0008255614,0.0004389342,0.0006118753,0.000009249541,0.0003132726,0.002125573],"genre_scores_gemma":[0.9982395,0.00000720331,0.001060054,0.000307168,0.00005917883,0.00001787743,2.992527e-7,0.00004153668,0.0002672139],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07032789,"threshold_uncertainty_score":0.9997602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0607103320586291,"score_gpt":0.3069362242740564,"score_spread":0.2462258922154273,"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."}}