As an undergraduate researcher under Dr. Panagiotis Tsiotras, this project focused on advancing active safety for autonomous vehicles in the F1Tenth competition format. Using an RC car equipped with an NVIDIA Jetson, LiDAR, and a ZED depth camera, the system employed ROS2 and Python to create a robust autonomous pipeline. SLAM was implemented for real-time race track mapping, followed by particle filter-based odometry for precise localization. Path following was achieved through a Pure Pursuit controller with PID for error correction, enabling the car to navigate dynamic tracks, avoid obstacles, and safely pass or avoid other autonomous vehicles. The project successfully demonstrated adaptive computer vision algorithms for car detection, object recognition, and real-time interaction in competitive racing environments.