DENOSAU: point cloud DENOising in adverSe weather conditions for AUtonomous driving

UTBM-Renault joint research project, 2019

DENOSAU logo DENOSAU is a joint research project between the Groupe Renault and the University of Technology of Belfort-Montbéliard (UTBM) in France, which assesses the impact of severe weather conditions on 3D lidar data and develop new solutions that can integrate with current hardware resources, extending the state-of-the-art to achieve efficient 3D lidar data denoising. Scientifically, DENOSAU aims to address one of the most pressing problems in autonomous driving, i.e. object detection and tracking, via the study of denoising method for point cloud generated by the 3D lidar under adverse weather conditions.

Team: Zhi Yan (PI), Yassine Ruichek (Co-I), Tao Yang

EPANer Robotics Team

Toyota Partner Robot joint research project, 2018-2021

EPANer logo The EPANer is a team representing the EPAN Research Group at the University of Technology of Belfort-Montbéliard (UTBM) in France, founded in 2018 to participate in robotics competitions, supported through Toyota Partner Robot joint research project. An important philosophy and research work of the EPAN Research Group is to advance technologies in the field of intelligent transportation systems (especially in autonomous driving) with a view to improving travel safety and productivity for human society. The EPANer robotics team will uphold this philosophy, while wishing to promote engineering and research in the field of service robotics, in order to meet the challenges such as aging population, labor costs and productivity, and natural disasters.

Team: Zhi Yan (PI), Yassine Ruichek, Nathan Crombez, Fahad Lateef

3L4AV: Lifelong learning of dynamic objects detection and tracking in adverse conditions for autonomous vehicles

PHC Barrande, 2018-2019

3L4AV logo 3L4AV is a mobility research project between the Czech Technical University in Prague (CTU) in Czechia and the University of Technology of Belfort-Montbéliard (UTBM) in France. The research goal is to provide new methods for autonomous vehicles to improve the robustness of their perception system, in particular for dynamic objects detection and tracking in adverse conditions, such as during adverse weather and dense traffic. In this project, we will investigate machine learning methods for multisensor systems deployed in autonomous vehicles. The heterogeneous nature of the sensory data will allow mutual training of the methods of dynamic object detection and tracking. On-the-fly, lifelong learning of the objects models for detection and tracking will be achieved through exploitation of the heterogeneity and amounts of data gathered by the vehicle's sensors over long periods of time. In contrast to existing technologies which are mainly based on static models, our project will focus on development of adaptive models that are completed and refined based on the data gathered over long-time operation of the autonomous vehicle.

Team: Yassine Ruichek (Coordinator), Zhi Yan (PI)

SAFER-LC: SAFER Level Crossing by integrating and optimizing road-rail infrastructure management and design

Horizon 2020 Research and Innovation action, 2017-2020 (€265k)

SAFER-LC logo Over the past few years, one person has been killed and close to one seriously injured every day on Level Crossings (LC). Therefore, the EU project SAFER-LC aims to improve safety and minimize risk by developing a fully integrated cross-modal set of innovative solutions and tools for the proactive management and design of LC infrastructure. EP users are responsible for developing an advanced video surveillance system for modeling and analyzing LC users' behavior including vulnerable users and their interactions with other users and to LC.

Team: Yassine Ruichek (PI), Zhi Yan, Jocelyn Buisson

Mobilitech : Transports du futur

Contrat de Plan État-Région (CPER), 2015-2022

utbm_robocar The Mobilitech platform is an initiative of the UTBM, within the framework of the project "UTBM 2020: European Engineering Campus" aiming at the establishment on the university campus of Montbéliard, a partnership technology platform in the field of transport and mobility.

Team: Yassine Ruichek (PI), Zhi Yan, Nathan Crombez, Jocelyn Buisson

SafePlatoon: Safe platoon of autonomous vehicles

French National Research Agency (ANR), 2011-2014

SafePlatoon logo[Video] The SafePlatoon project addresses the platooning problem, in the case of autonomous vehicles. It possesses an innovative aspect, given by the conception of a set of extended and robust platooning capabilities. Firstly, it is engaged with mastering platoon operation aspects, with different geometric configurations: linear, triangular, front line, etc. Secondly, it also concerns the conception of platoon's dynamic adaptation capabilities : configuration changes, vehicle insertion and ejection. An important aspect of the safe platoon project is that the proposed decision and control algorithms will be verified and validated. Verification consists in proving, by applying specific methods and tools, that a set of safety properties is valid relatively to a given system. Validation relates to performing, on a simulation model or a prototype of the system, a set of test scenarios. The latter being defined so as to evaluate the conformity and the quality of the proposed approaches. The SafePlatoon project partners possess an experience in the development of platooning decision and control algorithms and in the design of experimental intelligent vehicles. They have also participated in projects devoted to platooning and/or autonomous vehicle applications for urban transportation, agriculture and the military. Those experiences will be enriched and reinforced, in the frame of the SafePlatoon project.

Team: Franck Gechter (PI), Yassine Ruichek, Cyril Meurie, Cindy Cappelle, Frédérick Zann

PANsafer: Towards a safer level crossing

French National Research Agency (ANR), 2009-2012

PANsafer logo[Video] In compliance with the French call for tender ANR-VTT, PANsafer's main objective is to actively contribute to reduce level crossing accidents. To reach this objective, the project includes: 1) database analysis related to accidents that occurred in the most preoccupying level crossing s in France, and to determine their technical, human and organisational causes; 2) emphasis of main accident factors by identifying scenarios; 3) analysis of behaviours led by the infrastructure and its exploitation methods; 4) detection, recognition and evaluation of dangerous (or potentially) dangerous situations and setting up of a "criticality level"; 5) exploring the possibilities of new technical and organisational solutions, with a focus on the new technologies of detection of rail/road interactions environment, telecommunication and data exchange. PANsafer activities will directly allow the improvement and development of the intermodal rail/road collaboration. EPAN contributes on aspects concerning the perception of the environment by image analysis and virtual reality (3D modeling, simulation of scenarios, etc.).

Team: Yassine Ruichek (PI), Cyril Meurie, Houssam Salmane

IVT : Intelligence du Véhicule Terrestre

Contrat de Plan État-Région (CPER), 2007-2013

IVT logo

Team: Yassine Ruichek (PI), Cyril Meurie, Cindy Cappelle, Lijun Wei, You Li, Frédérick Zann

CAPLOC : Combinaison de l'Analyse d'images et la connaissance de la Propagation des signaux pour la LOCalisation

Programme de rcherche et d'innovation dans les transports terrestres (PREDIT), 2010-2013


Team: Yassine Ruichek (PI), Cyril Meurie, Julien Moreau, Dhouha Attia

ViLoc : Développement d'une plateforme de localisation par combinaison de capteurs GNSS et vidéo

Projet Région Franche-Comté, 2009-2012

ViLoc logo

Team: Cyril Meurie (PI), Yassine Ruichek, Dhouha Attia

Analyse d'images réelles et virtuelles pour la perception de l'environnement d'un mobile

PHC Volubilis, 2009-2012

Team: Yassine Ruichek (PI), Cyril Meurie, Cindy Cappelle, Youssef El merabet, Safaa Moqqaddem