{"id":"W4408992910","doi":"10.1007/s42154-024-00313-z","title":"Cascaded Safety Analysis and Test Scenario Generation Techniques for Autonomous Driving: A Case Study with WATonoBus","year":2025,"lang":"en","type":"article","venue":"Automotive Innovation","topic":"Safety Systems Engineering in Autonomy","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Hazard and operability study; Operability; Reliability engineering; Computer science; Reliability (semiconductor); Systems engineering; Process (computing); Hazard; Field (mathematics); Function (biology); Fault tree analysis; System safety; Scenario testing; Risk analysis (engineering); Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001367685,0.0005399126,0.0002533684,0.0007878743,0.0006605545,0.0006832435,0.0009145553,0.0006828001,0.001469297],"category_scores_gemma":[0.003543542,0.0002289823,0.000394478,0.000451001,0.000839838,0.0007648286,0.0007547577,0.0006975676,0.0002391536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007726221,"about_ca_system_score_gemma":0.001149717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004047086,"about_ca_topic_score_gemma":0.008213475,"domain_scores_codex":[0.9991737,0.0003344593,0.00003431104,0.00008941957,0.0002678447,0.0001002366],"domain_scores_gemma":[0.9975137,0.001555589,0.0001508148,0.0002952216,0.0003070952,0.0001776636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001290226,0.002674101,0.05549851,0.0007319693,0.0001428755,0.0116849,0.00840484,0.4658535,0.06793135,0.02343117,0.004834203,0.3575223],"study_design_scores_gemma":[0.0003025687,0.003129784,0.01800536,0.0001134697,0.0001112851,0.002716418,0.005341092,0.8651922,0.06310722,0.01377071,0.02809834,0.0001115071],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9078727,0.0001199492,0.08476863,0.0002527992,0.00001944775,0.0004036871,0.0001805379,0.0004637647,0.005918402],"genre_scores_gemma":[0.9561354,0.00006005064,0.04226727,0.00002236707,0.000003612999,0.0001179343,0.0001186133,0.00003990359,0.001234757],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004047086,"threshold_uncertainty_score":0.008047104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01106884570204591,"score_gpt":0.2465782635104031,"score_spread":0.2355094178083572,"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."}}