Radar signal processing has become a fundamental technology for autonomous vehicles because of its ability to provide reliable object detection and tracking under diverse environmental conditions, including rain, fog, snow, and low-light scenarios. Conventional radar systems often face challenges such as clutter, noise, multipath interference, and limited target resolution, which can affect the accuracy of perception. This paper presents an advanced radar signal processing framework for autonomous vehicles that integrates adaptive preprocessing, target detection, clutter suppression, feature extraction, and object classification to improve perception performance. The proposed approach employs digital signal processing techniques combined with deep learning-based classification to accurately identify vehicles, pedestrians, cyclists, and other road obstacles from radar data. Multi-target tracking algorithms are incorporated to estimate object position, velocity, and trajectory in real time, enabling safe navigation and collision avoidance. Experimental evaluation demonstrates that the proposed framework achieves higher detection accuracy, improved target localization, and robust performance under challenging weather and traffic conditions while maintaining low computational complexity suitable for real-time deployment. The proposed radar signal processing system enhances the reliability, safety, and efficiency of autonomous driving by providing accurate environmental perception for intelligent decision-making.