Extreme scenarios in autonomous driving, as well as unpredictable human behaviors, have become key factors restricting the development of Autonomous Driving Systems (ADS). Therefore, effectively generating safety-critical scenarios is crucial for enhancing the safety of ADS. Existing methods for generating autonomous driving scenarios mainly rely on substantial road-collecting and employ data-driven approaches combined with scenario generalization techniques, These methods are time-consuming, labor-intensive, and costly, making it difficult to effectively generate edge cases. In contrast, model-driven scenario modeling methods can construct logical scenario models to encapsulate complex scene features and effectively generate safety-critical scenarios. However, The challenging issue it faces is how to design a domain-knowledge-based visual scenario modeling language that supports the abstract modeling of complex driving scenarios and further effectively explores edge-critical scenarios To address these issues, this paper proposes an approach for scenario modeling with SML4ADS2.0 and edge-critical scenario generation for autonomous driving system. This method utilizes ontology modeling of scenarios within the ADS domain, combining formal quantitative evaluation with importance sampling to generate edge-critical scenarios. First, we propose a model-driven scenario modeling method based on SML4ADS2.0, designing a Scenario Modeling Language for Autonomous Driving System (SML4ADS2.0) to construct models of autonomous driving scenarios. Secondly, we implement the conversion of scenario models to Stochastic Hybrid Automata (SHA) through model transformation rules and use the model checking tool UPPAAL-SMC for quantitative evaluation and analysis of the scenario models. Subsequently, we employ importance sampling techniques to rapidly detect edge scenarios within the scenario space, effectively generating specific edge-critical scenarios from logical scenes. Finally, we demonstrate the method with case studies involving typical scenarios such as lane changes and overtaking. Experimental results indicate that this approach can effectively model scenarios and address the generation of safety-critical scenarios for ADS.