IRT SystemX will take part in the 14th ITS European Congress, the European congress on intelligent transport systems, from 30 May to 1 June in Toulouse.
This internationally recognised event is organised each year by ERTICO – ITS Europe, the platform for collaboration between ITS stakeholders in Europe. This congress will bring together players from industry, research, public authorities, infrastructure managers and users, i.e. all stakeholders in intelligent transport systems who work together to develop and deploy the mobility solutions of tomorrow.
IRT SystemX will share on its stand its research work on the cybersecurity of CCAM (Connected, Cooperative and Autonomous Mobility), including in particular the aspects of misbehavior detection for collective perception (TAM project), methodology and demonstration of security (3SA and SAM projects), and will present its MOSAR platform for the evaluation of autonomous systems and its ADScene industrialisation project.
Challenges and opportunities of cyber-resilience: an interdisciplinary forum to navigate between state-of-the-art and regulation landscapes (in-person event). The increase in competitiveness leads to a race to digitization. Critical infrastructures become highly connected to the cyber world. This is especially true for Cyber-Physical Systems (CPSs), which combine hardware and software components. However, these complex systems remain…
Raphaël Frank (Interdisciplinary Centre for Security, Reliability and Trust (SnT), Université du Luxembourg) ran a Seminar@SystemX on the topic "Infrastructure Assisted Cooperative and Distributed Perception Strategies for Connected Vehicles", on April 29, 2025, from 2:00 pm to 3:00 pm in SystemX premises at Digiteo Moulon and in webinar. Resume The research presented in this presentation…
Pietro Gori (Professeur, Télécom Paris) will run a Seminar@SystemX on the topic "Contrastive Learning in Computer Vision and Medical Imaging - A metric learning approach", on July 1st, 2025, from 2pm to 3pm. > Connect to the webinar << Abstract Contrastive Learning (CL) is a paradigm designed for self-supervised representation learning which has been applied…