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.

翻译：本科学出版物聚焦于自动驾驶领域中基于运动学的安全关键驾驶场景下边界值分析在边缘案例测试中的高效应用。边缘案例作为罕见但关键的驾驶情境，对自动驾驶系统的可靠性与安全性构成显著挑战。本文强调采用边界值分析这一系统化技术识别关键边界与取值的重要性，旨在实现全面的测试覆盖率。通过识别并测试极端边界条件（如最小安全距离等参数），本研究致力于提升自动驾驶系统在安全关键场景中的性能表现与鲁棒性。文中提出的方法与见解可为研究人员、开发人员及监管机构提供指导，助其有效应对边缘案例带来的挑战，确保自动驾驶系统在实际道路条件下的可靠性与安全性。