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Virtual simulation‐based testing of autonomous vehicles (AVs) needs massive challenging corner cases to reach high testing accuracy. Current methods achieve this goal by finding testing scenarios with low sampling frequency in the empirical distribution. However, these methods neglect modeling heterogeneous driving behavior, which actually is crucial for finding corner cases. To fill this gap, we propose an interpretable and operable method for sampling corner cases. Firstly, we initialize a testing scenario and allocate testing tasks to AV. Then, to simulate the variability in driving behaviors, we design utility functions with several hyperparameters and generate aggressive, conservative, and normal driving strategies by adjusting hyperparameters. By changing the heterogeneous driving behavior of surrounding vehicles (SVs), we can sample the challenging corner cases in the scenario. Finally, we conduct a …