\emph{Autonomous driving systems (ADS)} are safety-critical and require comprehensive testing before their deployment on public roads. While existing testing approaches primarily focus on the criticality of scenarios, they often overlook the diversity of the generated scenarios that is also important to reflect system defects in different aspects. To bridge the gap, we propose Legend, that features a top-down fashion of scenario generation: it starts with abstract functional scenarios, and then step downwards to logical and concrete scenarios, such that scenario diversity can be controlled at the functional level.
However, unlike logical scenarios that can be formally described, functional scenarios are often documented in natural languages (e.g., accident reports) thus cannot be precisely parsed and processed by computers; to tackle that issue, Legend leverages the recent advances of large language models to transform functional scenarios in natural languages to formal logical scenarios. To mitigate the distraction of useless information in functional scenarios, we devise a two-stage transformation that features the use of an intermediate language; consequently, we adopt two LLMs in Legend, one for extracting information from functional scenarios, the other for converting the extracted information to formal logical scenarios. We experimentally evaluate Legend on Apollo, an industry-grade ADS from Baidu. Evaluation results show that Legend can effectively identify critical scenarios, and compared to baseline approaches, Legend exhibits evident superiority in diversity of generated scenarios. Moreover, we also demonstrate the advantages of our two-phase transformation framework, and the accuracy of the adopted LLMs.