Road markings have been standardized for human perception for over a century. With the rapid expansion of autonomous vehicles that rely on machine vision, new challenges emerge: markings optimized for human drivers may fail automated perception systems under low lighting, adverse weather, or high retroreflectivity conditions. Drawing on imaging science, vehicle dynamics, and experimental results from road-paint sample analysis, we identify four critical design factors, including spatial characteristics, color, contrast, and retroreflectivity, and provide concrete recommendations for evolving road marking standards. These augment rather than replace existing human-centric requirements and are developed in alignment with the IEEE P2020 Automotive Image Quality Standards working group.