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 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.
Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles Testing
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
SYNTHESIZING TRAFFIC SCENARIOS FROM FORMAL SPECIFICATIONS FOR TESTING AUTOMATED VEHICLES
British Library Conference Proceedings | 2020
|Testing Scenarios for Human Factors Research in Level 3 Automated Vehicles
Springer Verlag | 2017
|AUTOMATIC GENERATION OF CORNER SCENARIOS DATA FOR TUNING AUTONOMOUS VEHICLES
European Patent Office | 2024
|