One of the challenges in developing autonomous vehicles (AV) is the collection of suitable real environment data for the training and evaluation of machine learning algorithms for autonomous vehicles. Such environment data collection via various sensors mounted on AV is big data in nature which require massive time and money investment and in some specific scenarios could pose a significant danger to human lives. This necessitates the virtual scenarios simulator to simulate the real environment by generating big data images from a virtual fisheye lens that can mimic the field of view and radial distortion of commercial available camera lens of any manufacturer and model. In this paper, we proposed the novelty of developing a fisheye lens with distortion system to generate big data scenarios images to train and test imaged based sensing functions and to evaluate scenarios according to EuroNCAP standards. A total of 10,123 RGB, depth and segmentation images of varying road scenarios were generated by proposed system in approximately 14 hours as compared to existing methods of 20 hours, achieving 42.86% improvement.