Predictive Vehicle Safety—Validation Strategy of a Perception-Based Crash Severity Prediction Function
نویسندگان
چکیده
Traffic accident avoidance and mitigation are the main targets of research vehicle safety development worldwide. Despite improving advanced driver assistance systems (ADAS) active systems, it will not be possible to avoid all accidents in near future. Innovative Pre-Crash (PCS) should contribute unavoidable accidents. However, there no standardized testing methods for systems. In particular, irreversible lead great challenges verification validation (V&V) process. The reliable precise real-time crash severity prediction (CSP) is, however, basic prerequisite PCS activation. This study proposes a novel assessment strategy perception-based function. doing so, intended functionality, requirements worked out context ISO 26262 ISO/PAS 21448 standards. order reduce effort, real-data-driven scenario-based approach is applied. Therefore, authors present unsupervised machine learning methodology creation concrete logical test scenario catalogs based on K-Means++ k-NN algorithms. developed used GIDAS database extract 35 representative clusters car collision scenarios, which utilized virtual testing. limitations presented method disclosed afterwards help future set right focus.
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ژورنال
عنوان ژورنال: Applied sciences
سال: 2023
ISSN: ['2076-3417']
DOI: https://doi.org/10.3390/app13116750