Test case sampling optimization for safety validation of automated driving systems

Abstract Testing and validating automated driving systems require carefully designed test cases that capture the complexity of real-world driving conditions. However, the inherent complexity of driving environments and the rarity of safety-critical situations pose significant challenges to developing reliable and efficient validation frameworks. This paper addresses these issues by selecting appropriate test cases from the largest-scale naturalistic driving study. We introduce a Kernel Test Case Sampling method, which selects cases satisfying two key criteria: representativeness, ensuring alignment with real-world scenarios, and coverage, capturing high-risk corner cases. To demonstrate the proposed method, it is applied to large-scale naturalistic driving study data. By selecting a limited number of cases, the method effectively captures long-tailed scenarios while approximating the distribution of naturalistic driving conditions. The sampling framework also enables robust accident-rate estimation, thereby ensuring fair comparisons across human driving performance and multiple systems. The proposed method supports standardized and scalable automated driving system safety validation, facilitating accelerated development and deployment while building public trust and regulatory confidence.