Predictive Vehicle Safety-Validation Strategy of a Perception-Based Crash Severity Prediction Function

Traffic accident avoidance and mitigation are the main targets of accident research and vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) and active safety systems, it will not be possible to avoid all vehicle accidents in the near future. Innovative Pre-Crash systems (PCS) should contribute to the accident mitigation of unavoidable accidents. However, there are no standardized testing methods for Pre-Crash systems. In particular, irreversible Pre-Crash systems lead to great challenges in the verification and validation (V&V) process. The reliable and precise real-time crash severity prediction (CSP) is, however, the basic prerequisite for irreversible PCS activation. This study proposes a novel validation and safety assessment strategy for a perception-based crash severity prediction function. In doing so, the intended functionality, safety and validation requirements of PCS are worked out in the context of ISO 26262 and ISO/PAS 21448 standards. In order to reduce the testing effort, a real-data-driven scenario-based testing approach is applied. Therefore, the authors present a novel unsupervised machine learning methodology for the creation of concrete and logical test scenario catalogs based on K-Means++ and k-NN algorithms. The developed methodology is used on the GIDAS database to extract 35 representative clusters of car to car collision scenarios, which are utilized for virtual testing. The limitations of the presented method are disclosed afterwards to help future research to set the right focus.