Abstract
In recent years, with the development of artificial intelligence, Unmanned Autonomous Systems (UAS) have also developed rapidly. It is necessary to conduct sufficient testing and evaluation of UAS before using it. This paper proposes a method for generating error-caused scenarios that lead to incorrect behavior in UAS, and how to evaluate the level of incorrect behavior in UAS caused by the scenario. Based on the method proposed in this paper, error-caused scenarios can be generated in the simulation platform, and the dataset constructed from these scenarios can be input into the target detection model to verify the effectiveness of error-caused scenarios. Our experiments have shown that the scenarios generated by the method can easily lead to errors in UAS, and the method can play an important role in the field of model-based UAS testing and validation.
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