Data Driven Quantitative Evaluation of Safety Controllability for Automated Vehicle

With the development of automated vehicle (AV), 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 AVs. Safety analysis is conducted to identify the potential hazard events. Taking system function and architecture into consideration, the failure modes of the vehicle hazards are identified with hazardous driving situation. Based on the identified failure modes, fault injection tests are conducted with critical scenarios. According to the vehicle dynamic performance, 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 with vehicle dynamic performance. The purposed method can quantitatively evaluate the controllability of the AVs with critical system failure in critical scenario, which can provide the guideline for autonomous driving system safety design.