Autonomous Vehicles Testing Considering Utility-Based Operable Tasks

Virtual simulation testing of Autonomous Vehicles (AVs) is gradually being accepted as a mandatory way to test the feasibility of driving strategies for AVs. Mainstream methods focus on improving testing efficiency by extracting critical scenarios from naturalistic driving datasets. However, the criticalities defined in their testing tasks are based on fixed assumptions, the obtained scenarios cannot pose a challenge to AVs with different strategies. To fill this gap, we propose an intelligent testing method based on operable testing tasks. We found that the driving behavior of Surrounding Vehicles (SVs) has a critical impact on AV, which can be used to adjust the testing task difficulty to find more challenging scenarios. To model different driving behaviors, we utilize behavioral utility functions with binary driving strategies. Further, we construct a vehicle interaction model, based on which we theoretically analyze the impact of changing the driving behaviors on the testing task difficulty. Finally, by adjusting SV's strategies, we can generate more corner cases when testing different AVs in a finite number of simulations.