Data Driven Quantitative Evaluation of Safety Controllability for Highly Automated Vehicle

With the development of Highly Automated Vehicles (HAVs), it is essential to ensure their safety even in the presence of system faults or function inefficiency. Safety controllability refers to the ability to manage and control the vehicle, ensuring that it remains safe even in the presence of faults with unexpected conditions. This study proposed a data driven method to evaluate quantitatively safety controllability for HAVs. Safety analysis is conducted to identify the potential hazard events. Considering system function and architecture, the failure modes of the vehicle hazards are identified with critical scenarios. Based on the identified failure modes, fault injection tests are conducted with critical scenarios. Furthermore, the improved Analytic Hierarchy Process (AHP) can be explored to quantitatively evaluate the safety controllability based on fault injection test results. In particular, this study focuses on the case study to forecast the effect of faults on safety controllability in concrete scenario. The purposed method can quantitatively evaluate the controllability of the HAVs with critical system failure in critical scenario, which can provide the guideline for autonomous driving system safety design.