High dynamic range imaging aims to recover the luminance and color information of real-world scenes, thereby overcoming the common problems of highlight saturation and shadow detail loss in conventional sensor imaging. It has been extended to applications such as autonomous driving and virtual reality/augmented reality. However, artifact removal in dynamic scenes remains a central challenge. To address this issue, the related datasets and evaluation metrics were systematically reviewed, the major research advances were comprehensively summarized, and the inherent causes of imaging model deficiencies and the current technical bottlenecks were further analyzed. It also compared and analyzed the performance differences among existing state-of-the-art methods from the perspectives of model generalization ability, computational complexity, and inference time. Building on recent development trends, three levels of important research directions were further identified, namely fundamental core challenges, key performance optimization, and frontier technology exploration, with the aim of providing a useful reference for both academic research and engineering practice.