  This study underscores the vital importance of intelligent driving functions
in enhancing road safety and driving comfort. Central to our research is the
challenge of obtaining sufficient test data for evaluating these functions,
especially in high-risk, safety-critical driving scenarios. Such scenarios
often suffer from a dearth of available data, primarily due to their inherent
complexity and the risks involved.
  Addressing this gap, our research introduces a novel methodology designed to
create a wide array of diverse and realistic safety-critical driving scenarios.
This approach significantly broadens the testing spectrum for driver assistance
systems and autonomous vehicle functions. We particularly focus on the
follow-up drive scenario due to its high relevance in practical applications.
Here, vehicle movements are intricately modeled using kinematic equations,
incorporating factors like driver reaction times. We vary parameters to
generate a spectrum of plausible driving scenarios.
  The utilization of the Difference Space Stopping (DSS) metric is a pivotal
element in our research. This metric plays a crucial role in the safety
evaluation of follow-up drives, facilitating a more thorough and comprehensive
validation process. By doing so, our methodology enhances the reliability and
safety assessment of driver assistance and autonomous driving systems,
specifically tailored for the most challenging and safety-critical scenarios.
