With the development of driving simulators, graphics engines and synthetic-to-real domain adaptation algorithms, synthetic datasets become increasingly more photo-realistic. The advancement of such dataset is crucial for advanced driving systems, particularly for training learning-based methods and validation. An important consideration is around the fidelity of synthetic datasets, particularly regarding their suitability for deep learning applications such as object detection or segmentation. However, quantifying fidelity poses a significant challenges. To address this gap, we propose a set of fidelity scores to quantify the level of fidelity of RGB images from these datasets. Through in-depth examination, we aim to reveal information about the texture patterns and high-frequency components that contribute to the objective perception of data realism in road scenes. Furthermore, a multi-criteria combination using belief theory is performed to merge these scores and give a global score involving the level of fidelity, the level of uncertainty on this decision, and the level of conflict between the scores.

In a large set of applications, belief theory is applied to handle and to manage efficiently uncertainty and conflict. An essential step consists in choosing the appropriate Basic Belief Function (BBF) to generate Basic Belief Assignments (BBA) before the combination stages. In this context, we introduce a novel method that leverages belief theory to generate BBA using a parametric family. This approach offers a structured framework for evaluating objective criteria and selecting the most suitable BBF for a given scenario. The method is designed to accommodate a wide range of applications, from decision-making in uncertain environments to data fusion in complex systems. The key advantage of our method lies in its flexibility and adaptability. By using a parametric family of functions, the method can tailor the BBA generation process to specific requirements, such as the level of noise or discordance in the sources. This allows us to optimize the performance of the fusion architecture and improve decision-making accuracy. Our method was applied to select the most suitable candidate from a set of functions, aiming to minimize the effects of noise and discordance. This involved evaluating the performance of each function against a set of objective criteria, such as robustness and reliability. The results highlighted that our method outperformed existing approaches, demonstrating its effectiveness in generating BBAs that are well-suited to the task at hand. In conclusion, our method offers a new level of adaptability and generality in BBA generation, enabling the customization of hyperparameters to optimize data fusion processes.