As global vehicle numbers rise, traffic accidents pose significant threats to life and property, with approximately 1.3 million casualties annually. To mitigate accidents, automotive Original Equipment Manufacturers and suppliers are prioritizing active safety technologies like the automatic emergency steering (AES) system, which proactively intervenes in vehicle dynamics to avoid collisions. The AES system, as an important component of active safety, lacks systematic testing and evaluation methods. We aim to conduct a more comprehensive and scientific pre-evaluation of the AES system, which realizes its functions through onboard sensors, decision control systems, and actuators. The environmental awareness system uses sensors such as a camera, millimeter wave radar, and laser radar to collect surrounding information. Based on the perceived data, the decision control system generates the optimal steering strategy through hazard identification and risk assessment. This study addresses these gaps by analyzing AES performance indices and existing regulations (e.g., UN R79, ISO 23375, IIHS, E-NCAP) to design a comprehensive test framework. Four high-coverage scenarios were developed: longitudinal scooter conflicts, vehicle cut-ins, pedestrian crossings, static cone bucket, and misoperation validation, ensuring evaluation under diverse conditions. It has important practical significance for improving China’s automotive safety technology level and reducing road traffic accident casualties. With the rapid development of autonomous driving technology, this research achievement will also provide technical support for the development of higher-level intelligent driving systems.