Pierre Duthon

Centre d’études et d’expertise sur les risques, l’environnement, la mobilité et l’aménagement | CEREMA · DLCF

Accurate luminance-based image generation is critical in physically based simulations, as even minor inaccuracies in radiative transfer calculations can introduce noise or artifacts, adversely affecting image quality. The radiative transfer simulator, SWEET, uses a backward Monte Carlo approach, and its performance is analyzed alongside other simul...

FR : L’utilisation du spectre SWIR (Short Wave Infrared), compris généralement entre 0,7 et 2,5 µm, a ouvert de nouvelles perspectives dans le domaine de l’imagerie, et ce plus particulièrement dans le développement des véhicules autonomes (AV). Alors que les solutions actuelles de détection d’obstacles démontrent une efficacité appréciable sous de...

Vehicles featuring partially automated driving can now be certified within a guaranteed operational design domain. The verification in all kinds of scenarios, including fog, cannot be carried out in real conditions (risks or low occurrence). Simulation tools for adverse weather conditions (e.g., physical, numerical) must be implemented and validate...

Citation: Salmane, P.H.; Rivera Velázquez, J.M.; Khoudour, L.; Mai, N.A.M.; Duthon, P.; Crouzil, A.; Saint Pierre, G.; Velastin, S.A. 3D Object Detection for Self-Driving Cars Using Video and LiDAR: An Ablation Study. Sensors 2023, 23, 3223. https:// Abstract: Methods based on 64-beam LiDAR can provide very precise 3D object detection. However, hig...

Improving the reliability of automotive perceptive sensors in degraded weather conditions, including fog, is an important issue for road safety and the development of automated driving. Cerema has designed the PAVIN platform reproducing fog and rain conditions to evaluate optical automotive sensor performance under these conditions. In order to inc...

Object detection is recognized as one of the most critical research areas for the perception of self-driving cars. Current vision systems combine visible imaging, LIDAR, and/or RADAR technology, allowing perception of the vehicle’s surroundings. However, harsh weather conditions mitigate the performances of these systems. Under these circumstances,...

Today, the popularity of self-driving cars is growing at an exponential rate and is starting to creep onto the roads of developing countries. For autonomous vehicles to function, one of the essential features that needs to be developed is the ability to perceive their surroundings. To do this, sensors such as cameras, LiDAR, or radar are integrated...

The role of sensors such as cameras or LiDAR (Light Detection and Ranging) is crucial for the environmental awareness of self-driving cars. However, the data collected from these sensors are subject to distortions in extreme weather conditions such as fog, rain, and snow. This issue could lead to many safety problems while operating a self-driving...

In road environments, real-time knowledge of local weather conditions is an essential prerequisite for addressing the twin challenges of enhancing road safety and avoiding congestions. Currently, the main means of quantifying weather conditions along a road network requires the installation of meteorological stations. Such stations are costly and m...

The ability to accurately detect and localize objects is recognized as being the most important for the perception of self-driving cars. From 2D to 3D object detection, the most difficult is to determine the distance from the ego-vehicle to objects. Expensive technology like LiDAR can provide a precise and accurate depth information, so most studie...

Dear colleagues, Atmosphere is an international peer-reviewed open access monthly journal published by MDPI. Artificial vision systems, whether active or passive, are increasingly used for applications ranging from intelligent visual surveillance to automated driving. These systems are largely disrupted by adverse weather conditions such as fog, ra...

Atmosphere is an international peer-reviewed open access monthly journal published by MDPI. Artificial vision systems, whether active or passive, are increasingly used for applications ranging from intelligent visual surveillance to automated driving. These systems are largely disrupted by adverse weather conditions such as fog, rain, or snow. Sev...

Fog is one of major challenges for transportation systems. The automation of the latter is based on perception sensors that can be disrupted by atmospheric conditions. As fog conditions are random and non-reproducible in nature, Cerema has designed a platform to generate fog and rain on demand. Two types of artificial fog with different droplet siz...

This article focuses on analyzing the performance of a typical time-of-flight (ToF) LiDAR under fog environment. By controlling the fog density within CEREMA Adverse Weather Facility 1 , the relations between the ranging performance and fogs are both qualitatively and quantitatively investigated. Furthermore, based on the collected data, a machine...

https://hal.archives-ouvertes.fr/hal-02571657/document

Autonomous driving is based on innovative technologies that have to ensure that vehicles are driven safely. LiDARs are one of the reference sensors for obstacle detection. However, this technology is affected by adverse weather conditions, especially fog. Different wavelengths are investigated to meet this challenge (905 nm vs. 1550 nm). The influe...

Computer vision systems are increasingly present on roadways, both on the roadside and on board vehicles. Image features are an essential building block for computer vision algorithms in a road environment. Eight of the most representative image features in a road environment are selected on the basis of a literature review, and their robustness in...

This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks...

This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks...

Les systèmes de vision artificielle sont de plus en plus présents en contexte routier. Ils sont installés sur l'infrastructure, pour la gestion du trafic, ou placés à l'intérieur du véhicule, pour proposer des aides à la conduite. Dans les deux cas, les systèmes de vision artificielle visent à augmenter la sécurité et à optimiser les déplacements....

Computer vision systems are increasingly present in road environments. These have not been evaluated in adverse weather conditions, particularly in rain. The objective of this article is to develop tools to validate these computer vision systems in such adverse weather conditions. This study begins by setting up a digital rain image simulator based...

In the road context, objects of interest (salient or not) must be efficiently detected under any condition to ensure safety, for both driver assistance systems and autonomous vehicles. Nine representative state-of-the-art saliency models are evaluated on driving databases (human perception vs. robotics). Although not sufficient for robust detection...

Computer vision is increasingly present on the road, both on the infrastructure for traffic monitoring and in vehicles for driving assistance. Implemented algorithms use image features for many operations. Image features are therefore crucial. This state of the art reference computer vision algorithms of the road context under a new approach, putti...