{"id":"W3179442871","doi":"10.1109/cvpr46437.2021.00978","title":"AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles","year":2021,"lang":"en","type":"article","venue":"","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":166,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Toronto","funders":"","keywords":"Robustness (evolution); Computer science; Adversarial system; Lidar; Autonomy; Advanced driver assistance systems; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0004584513,0.0001582989,0.0002018782,0.00004982129,0.000524346,0.0003148261,0.0005578619,0.00008334591,0.00006441427],"category_scores_gemma":[0.001526131,0.0001575643,0.00009911245,0.0002954311,0.00002896216,0.0005552022,0.0005721517,0.0002414491,0.00002495401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007085909,"about_ca_system_score_gemma":0.0002110968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007150557,"about_ca_topic_score_gemma":0.00001824464,"domain_scores_codex":[0.9983015,0.0001393423,0.0003030985,0.0005496638,0.0002639706,0.0004424327],"domain_scores_gemma":[0.9982812,0.0008439115,0.00004917902,0.0004732491,0.0002314385,0.0001209897],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008210074,0.0001566497,0.001993195,0.0001095367,0.00006658536,0.0001463289,0.001320553,0.09658467,0.03763164,0.775257,0.001333901,0.08539171],"study_design_scores_gemma":[0.0003279356,0.00002965977,0.0001081937,0.00002926074,0.00001098638,0.00004065215,0.0000838803,0.9836342,0.007833199,0.001433904,0.006235318,0.0002328164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003786368,0.0001165335,0.9889702,0.003884147,0.000630743,0.0001110802,8.157137e-7,0.0005510153,0.001949091],"genre_scores_gemma":[0.2939456,0.000003663271,0.7047093,0.0006563662,0.0003043767,0.00001190701,0.000003167473,0.00001573586,0.0003499557],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8870495,"threshold_uncertainty_score":0.6425288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306856905423668,"score_gpt":0.2829613151278228,"score_spread":0.2698927460735861,"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."}}