Autonomous Driving Systems(ADS) require extensive evaluation of safety before they can come onto the market. However, since relying solely on field testing is practically infeasible due to the impossibility to cover sufficient distances to ensure adequate safety, the focus shifted to scenario-based testing. The challenge is to generate scenarios flexibly. We proposed Scenario Modeling Language for ADS (SML4ADS) as a Domain-Specific Modeling Language (DSML) for scenario representation and generation. Compared to other existing works, our approach simplifies the description of scenarios in a non-programming, user-friendly manner, allows modeling stochastic behavior of vehicles and generating executable scenario in CARLA. We apply SML4ADS in numerous typical scenarios to preliminarily demonstrate the effectiveness and feasibility of our approach in modeling and generating executable scenarios.