Practical validation of synthetic pre-crash scenarios

The representativeness of synthetic pre-crash scenarios is crucial for assessing the safety impact of Driving Automation Systems through virtual simulations. However, a gap remains in the robust evaluation of synthetic pre-crash scenarios' practical equivalence to their real-world counterparts; that is, whether they are similar enough for the intended assessment purpose. Conventional significance testing is inadequate, as it focuses on detecting differences rather than establishing practical equivalence. This study addresses the research gap by extending our previous work on a Bayesian Region of Practical Equivalence (ROPE)-based equivalence testing framework by introducing a binning-based approach to define appropriate statistics and equivalence criteria. Two novel binning-based statistics are proposed to quantify localized and aggregate distributional discrepancies between synthetic and reference datasets. The statistics incorporate bin weights derived from system re-simulation outcomes, enabling discrepancies in regions with greater influence on safety-impact estimates to be emphasized. The proposed approach further provides diagnostic insight into the distributional regions driving non-equivalence, facilitating targeted dataset generation and improvements. The framework's applicability is demonstrated through a case study, which tests the practical equivalence of two synthetic rear-end pre-crash datasets with a previously developed reference dataset for the safety impact assessment of an Automatic Emergency Braking system. The results show that the framework provides informative assessments of practical equivalence and diagnostic insights into the divergence of datasets. Although the demonstration focuses on rear-end pre-crash scenarios, the framework is generic and extensible to broader validation contexts, providing an interpretable and principled basis for practical equivalence assessment across diverse synthetic data applications.