  This scientific publication focuses on the efficient application of boundary
value analysis in the testing of corner cases for kinematic-based
safety-critical driving scenarios within the domain of autonomous driving.
Corner cases, which represent infrequent and crucial situations, present
notable obstacles to the reliability and safety of autonomous driving systems.
This paper emphasizes the significance of employing boundary value analysis, a
systematic technique for identifying critical boundaries and values, to achieve
comprehensive testing coverage. By identifying and testing extreme and boundary
conditions, such as minimum distances, this publication aims to improve the
performance and robustness of autonomous driving systems in safety-critical
scenarios. The insights and methodologies presented in this paper can serve as
a guide for researchers, developers, and regulators in effectively addressing
the challenges posed by corner cases and ensuring the reliability and safety of
autonomous driving systems under real-world driving conditions.
