Research on Quantitative Safety Risk Assessment Methods at Vehicle Controllability Level

In recent years, advancements in electrification, intelligent connectivity, and autonomous driving have made functional safety, particularly the vehicle-level controllability, a critical research focus. Although the ISO 26262 standard provides a systematic framework for automotive functional safety, challenges remain in accurately defining and quantifying controllability levels. In this paper, we analyze the influence of potential faults—such as unintended torque and loss of regen-erative braking force—on vehicle controllability under various operating conditions. A database of vehicle attitude data and corresponding controllability levels is established through real-vehicle tests. Feature screening is then performed to identify the most relevant attributes that significantly affect controllability. Based on this database, a controllability model is developed using the Support Vector Machine (SVM) method, which can effectively predict controllability levels. The model is applied in multiple simulation scenarios to assess vehicle controllability under different conditions. Additionally, the safety boundaries of motor torque are determined at varying speeds, providing insights into critical thresholds beyond which the vehicle's con-trollability may be compromised. This work presents a novel approach for the quantitative analysis of vehicle controllability and safety risks, offering a reliable tool for evaluating functional safety in modern vehicles.