<![CDATA[The rapid development of Artificial Intelligence (AI) has significantly transformed the automotive industry, particularly in active vehicle safety systems. AI enables vehicles to perceive their environment, make decisions in real time, predict risks, and adapt safety features according to driver characteristics. This study aims to analyze the development of AI in active automotive safety systems, identify its primary roles, evaluate its impact on accident prevention, and examine the implementation challenges from technical, regulatory, and ethical perspectives. This research employs a qualitative descriptive approach using a literature review method. Data were collected from scientific journals, conference proceedings, international standards, dissertations, and preprint repositories related to AI-based automotive safety technologies. The findings indicate that AI has evolved from simple object detection systems into advanced technologies capable of driver cognitive state inference, predictive safety analysis, and autonomous decision-making. AI-based systems such as Automatic Emergency Braking (AEB), Adaptive Cruise Control (ACC), and Driver Monitoring Systems (DMS) have demonstrated significant contributions to reducing accident risks and injury severity. However, challenges remain regarding adverse weather conditions, mixed traffic environments, model uncertainty, certification frameworks, and ethical accountability. The study concludes that AI has substantial potential to improve road safety, but successful implementation requires robust technical development, adaptive regulations, and comprehensive safety assurance mechanisms.]]>
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