As autonomous vehicles become increasingly advanced, ensuring their ability to detect obstacles and prevent accidents is critical. This investigation focuses on developing a robust and systematic method to evaluate the performance of Automatic Emergency Braking Systems in both normal and challenging driving conditions. AEBS relies on RADAR technology to spot potential collisions and automatically apply the brakes to prevent accidents. However, real-life driving conditions are often more complex, involving fog, heavy rain, or low light. Testing how AEBS performs in such situations is essential to ensure its reliability. This study contributes by creating a detailed testing framework for AEBS, designing specific driving scenarios to evaluate system responsiveness, and validating the framework through real-world testing. Instead of only confirming that the system works, this research also examines how AEBS performs in difficult conditions, such as poor visibility or night driving. The results demonstrate that while AEBS is generally effective at detecting obstacles and preventing collisions, its performance can vary when conditions become more demanding. By offering a structured testing approach, this study provides valuable insight into the strengths and weaknesses of the system. These insights can guide future improvements, making AEBS safer and more reliable in real-world driving