探索边界：自动驾驶背景下基于运动学的安全关键驾驶场景的全面软件测试
Exploring the Boundaries: Thorough Software Testing for Safety-Critical Driving Scenarios Based on Kinematics in the Context of Autonomous Driving
AI总结：
本文研究自动驾驶中基于运动学的安全关键边角场景测试，提出运用边界值分析识别最小距离等极端条件，以提升系统鲁棒性并为相关人员提供测试指导。
摘要：
本科学出版物聚焦于边界值分析在自动驾驶领域内基于运动学的安全关键驾驶场景的边角案例测试中的高效应用。边角案例代表罕见且关键的情况，对自动驾驶系统的可靠性和安全性构成显著障碍。本文强调采用边界值分析这一用于识别关键边界和数值的系统化技术以实现全面测试覆盖的重要性。通过识别并测试诸如最小距离等极端和边界条件，本出版物旨在提升自动驾驶系统在安全关键场景中的性能和鲁棒性。本文提出的见解和方法论可作为研究人员、开发人员和监管人员的指南，帮助其有效应对边角案例带来的挑战，并确保自动驾驶系统在真实驾驶条件下的可靠性和安全性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
