Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles Testing

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, the authors propose an interpretable and operable method for sampling corner cases. Firstly, the authors initialize a testing scenario and allocate testing tasks to AV. Then, to simulate the variability in driving behaviors, the authors 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), the authors can sample the challenging corner cases in the scenario. Finally, the authors conduct a series of simulation experiments in a typical lane-changing scenario. The simulation results reveal that by adjusting the occurrence frequency of heterogeneous SVs in the testing scenario, more corner cases can be found in limited rounds of simulations.


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  • Accession Number: 01846874
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 25 2022 9:35AM