Generating realistic road scenes is crucial for advanced driving systems, particularly for training deep learning methods and validation. Numerous efforts aim to create larger and more realistic synthetic datasets using graphics engines or synthetic-to-real domain adaptation algorithms. In the realm of computer-generated images (CGIs), assessing fidelity is challenging and involves both objective and subjective aspects. Our study adopts a comprehensive conceptual framework to quantify the fidelity of RGB images, unlike existing methods that are predominantly application-specific. This is probably due to the data complexity and huge range of possible situations and conditions encountered. In this paper, a set of distinct metrics assessing the level of fidelity of virtual RGB images is proposed. For quantifying image fidelity, we analyze both local and global perspectives of texture and the high-frequency information in images. Our focus is on the statistical characteristics of realistic and synthetic road datasets, using over 28,000 images from at least 10 datasets. Through a thorough examination, we aim to reveal insights into texture patterns and high-frequency components contributing to the objective perception of data realism in road scenes. This study, exploring image fidelity in both virtual and real conditions, takes the perspective of an embedded camera rather than the human eye. The results of this work, including a pioneering set of objective scores applied to real, virtual, and improved virtual data, offer crucial insights and are an asset for the scientific community in quantifying fidelity levels.

From an analysis of the priors used in state-of-the-art algorithms for single image defogging, a new prior is proposed to obtain a better atmospheric veil removal. Our hypothesis is based on a physical model, considering that the fog appears denser near the horizon rather than close to the camera. It leads to more restoration when the fog depth is more important, for a more natural rendering. For this purpose, the Naka–Rushton function is used to modulate the atmospheric veil according to empirical observations on synthetic foggy images. The parameters of this function are set from features of the input image. This method also prevents over-restoration and thus preserves the sky from artifacts and noises. The algorithm generalizes to different kinds of fog, airborne particles, and illumination conditions. The proposed method is extended to the nighttime and underwater images by computing the atmospheric veil on each color channel. Qualitative and quantitative evaluations show the benefit of the proposed algorithm. The quantitative evaluation shows the efficiency of the algorithm on four databases with different types of fog, which demonstrates the broad generalization allowed by the proposed algorithm, in contrast with most of the currently available deep learning techniques.

Developing and evaluating visual perception systems for autonomous vehicles requires data across diverse and adverse driving conditions, yet collecting and annotating such real-world data is costly and often impractical. To address this challenge, we propose a modular, scenario-driven framework for generating synthetic datasets tailored to the evaluation of visual perception functions. The framework aligns with the operational boundaries and detection–response requirements of automated driving functions and comprises three stages: (1) configuring use-case-driven scenarios, (2) generating sensor data and ground truth via simulation, and (3) post-processing to ensure dataset usability. Designed to be generic and flexible, the framework is instantiated and demonstrated through its integration with specific platforms and tools, namely Pro-SiVIC and RTMaps. We evaluate the generated dataset from two perspectives, image fidelity and perception performance under synthetic weather conditions, in comparison to real-world conditions. Furthermore, we train multiple perception models under different learning paradigms, including baseline, transfer-learning, and mixed-training strategies, to examine the influence of synthetic data on robustness. Experimental results demonstrate not only the high quality of the generated data but also its effectiveness in evaluating visual perception functions, as well as its benefit to model robustness and generalization.

With the development of AI-based perception algorithms using cameras, access to large and representative datasets is crucial. For autonomous driving systems, it is essential to use road context datasets covering the entire Operating Design Domain (ODD), including various road configurations, driving scenarios, and weather conditions.In this context, it is imperative to propose mechanisms and metrics that allow quantifying the fidelity and the level of representativeness of these simulated datasets, in order to evaluate and validate their usability for training and evaluation stages.In this paper, we propose an objective and multi-modal approach allowing to calculate 4 scores representing several aspects of the synthetic image fidelity. These scores address local, global and statistical texture analysis. In addition, a multi-criteria approach, based on evidence theory, is proposed to merge these scores to obtain a final global score. The result is the generation of a global score along with the uncertainty and conflict quantification. This method has been applied on a large number of real and virtual datasets in different weather conditions (clear, rainy, foggy). The initial results are promising and confirm the interest and the relevance of this method.

Video restoration for noise removal, deblurring or super-resolution is attracting more and more attention in the fields of image processing and computer vision. Works on video restoration with data-driven approaches for fog removal are rare however, due to the lack of datasets containing videos in both clear and foggy conditions which are required for deep learning and benchmarking. A new dataset, called REVIDE, was recently proposed for just that purpose. In this paper, we implement the same approach by proposing a new REal-world VIdeo dataset for the comparison of Defogging Algorithms (VIREDA), with various fog densities and ground truths without fog. This small database can serve as a test base for defogging algorithms. A video defogging algorithm is also mentioned (still under development), with the key idea of using temporal redundancy to minimize artefacts and exposure variations between frames. Inspired by the success of Transformers architecture in deep learning for various applications, we select this kind of architecture in a neural network to show the relevance of the proposed dataset.