<p dir="ltr">Recent advancements in autonomous vehicle research and development have significantly enhanced their capabilities, largely due to innovations in sensor technology. Infrared (IR) sensing, in particular, has rapidly advanced to the point where sensors have been miniaturized and made commercially viable for integration into autonomous vehicles. While hardware advancements are notable, large-scale deployment requires rigorous testing to ensure reliability and safety.</p><p dir="ltr">Autonomous vehicle technology heavily relies on machine learning (ML) and object detection algorithms, which necessitate precisely annotated image data for effective training. Although IR sensors are commercially available, their acquisition at scale remains economically challenging, making it difficult to generate the necessary volume of data. Furthermore, annotating this data is resource-intensive, requiring significant human effort.</p><p dir="ltr">This research addresses these challenges by proposing a cost-effective and scalable solution: developing a virtual IR model within a virtual simulation environment using the hyper-realistic open-source graphics engine, Unreal Engine. While some proprietary solutions offer virtual IR sensing simulations, there is a significant gap in open-source options that are economically accessible to most researchers.</p><p dir="ltr">The proposed IR camera model is designed using Unreal Engine’s blueprint scripting and open-source object models, creating a virtual simulation environment capable of generating auto-annotated IR images at a rate of 60 frames per second. These images are then used to train a YOLO object detection model, which is subsequently applied to open-source real infrared images, simulating the actual use of IR camera-based object detection in autonomous vehicles. The resulting model demonstrates promising potential in providing a user-friendly, open-source virtual IR camera that can generate annotated images suitable for training object detection models.</p>