This presentation explores the influence of optical design on the cost, complexity, and data fusion abilities of thermal imaging cameras used in AI-driven Pedestrian Automatic Emergency Braking (PAEB) systems. Emphasizing the importance of thermal imaging for road safety, particularly for detecting vulnerable road users (VRUs) in different lighting conditions, the study presents a framework to examine the interplay between field of view, f-number, sensor resolution, and pixel pitch, and their collective effect on thermal image quality, optical assembly complexity, and cost for fusion with other sensors. The findings suggest that by optimizing these parameters, it’s possible to enhance VRU detection, manage optical assembly complexity, and significantly decrease system costs. The paper argues that thermal imaging cameras for PAEB fusion systems can be produced much more cost-effectively than current thermal imaging-based night vision systems for cars.