Recent research in localization systems for Vehicular Ad Hoc Networks (VANETs) emphasizes improving positioning accuracy, robustness, and reliability under dynamic vehicular and urban conditions. Traditional GPS-based methods are being enhanced with cooperative localization, where vehicles exchange position, distance, and signal strength information to refine estimates in real time. Machine learning and deep learning models, such as convolutional and recurrent neural networks, are employed to correct GPS errors and predict vehicle positions in non-line-of-sight or signal-blocked environments. Hybrid systems combining GPS, inertial sensors, LiDAR, radar, and vehicular communication data have been developed to achieve centimeter-level precision even in dense cityscapes. Edge computing and 5G-assisted architectures further enable low-latency data fusion and distributed localization. Additionally, blockchain and trust-based frameworks are being integrated to secure localization data exchange against spoofing and falsification attacks. These advances collectively pave the way for high-accuracy, resilient, and intelligent localization mechanisms essential for autonomous and safety-critical VANET applications.