Distracted driving presents a substantial threat to traffic safety, frequently arising from drivers’ attention lapses due to various factors. Among these, the road landscape may significantly contribute to distracted driving. Eye tracking is a fundamental tool in the study of distracted driving; however, traditional eye movement analysis lacks robust methodologies for evaluating objects within the visual field, particularly standalone objects that appear transiently during a trip. To address this gap, we propose a novel framework for analyzing driver attention, integrating eye movement data with object detection algorithms. Specifically, we employ the YOLO object detection algorithm to enhance the analysis of eye movement data, thereby streamlining the annotation process and improving analytical efficiency. This framework can be instrumental in assessing the functionality and impact of road facilities prior to their construction.