GNSS in Cooperative, Connected and Automated Driving: Benefits, Advantages, Limitations and Threats

Global Navigation Satellite Systems (GNSS) are a cornerstone of positioning, navigation and timing (PNT) for cooperative, connected and automated driving (CCAD). This talk reviews the role of GNSS in CCAD, highlighting its key benefits and advantages while critically examining its main limitations, including signal blockage in urban canyons and tunnels, multipath and non‑line‑of‑sight errors, and dependence on external correction infrastructure. The presentation also addresses emerging threats, in particular jamming and spoofing attacks that can compromise localization integrity and V2X safety functions, and discusses their implications for system resilience and security. Finally, we outline mitigation strategies and architectural recommendations, such as multi‑sensor fusion, integrity monitoring, and trustworthy GNSS processing, to enable robust and scalable deployment of GNSS‑based PNT in future CCAD systems.

Speaker biography:

José Eugenio Naranjo (PhD in Computer Science) is Professor of the Universidad Politécnica de Madrid. His research area includes Intelligent Systems and Automation Engineering, Intelligent Transport Systems, intelligent control and autonomous vehicle navigation, focusing on defense applications. Prof. Naranjo is principal investigator of more than 20 research projects, as part of the University Institute for Automobile Research (INSIA) and is author of more than 100 scientific publications. As part of these projects, prof. Naranjo has experience in AI/ML platforms, ML/AI models and associated challenges. Prof. Naranjo has received five awards in the field of research of intelligent and automated mobility. Prof. Naranjo is a member of several scientific and editorial committees, among which we can highlight his role as editor of the IEEE Transactions on Intelligent Transportation Systems magazine, belonging to the organizing committees of the most relevant conferences in the sector such as the IEEE Intelligent Vehicle Symposium and the IEEE Intelligent Transportation Systems Conference. He holds the position of Vice President in the Spanish Committee of IEEE-ITS. Additionally, Prof. Naranjo belongs to the panel of experts of the European Commission for the area of ICT for Transport and GNSS-Galileo, serving as an evaluator and reviewer of European projects from 2007 to the present, in FP7, H2020, HE, including EDF and NATO, exploiting excellent writing and communication skills. Prof. Naranjo has also experience in collaboration with defense industry and the Spanish Ministry of Defense on for the conduct of scientific and experimentation on technologies focused to Spanish Army.

Robustness through Cooperation: Localization for Partially Connected Networks

Reliable and precise positioning information is paramount in a plethora of applications involving distributed sensing systems or networks of interconnected agents. This talk focuses on the centralized cooperative localization problem exploiting inter-agent ranging observations, where: i) only a subset of distances is available, i.e., the network is partially connected, and ii) a position estimate is available for a subset of agents. We propose a maximum likelihood approach that, in contrast to standard MDS-MAP or semidefinite programming techniques, is asymptotically optimal in the mean square error sense. Illustrative examples are provided to support the discussion.

Speaker biography:

Jordi Vilà-Valls is Professor at ISAE-SUPAERO, University of Toulouse, France. He received the Ph.D. (2010) in signal processing from the Grenoble Institute of Technology (INPG), Grenoble, France, and the HDR (2021) from the Toulouse Institute of Technology (INPT), Toulouse, France. His primary areas of interest include statistical signal processing, estimation/detection theory, Bayesian inference and robust filtering, with applications to localization, tracking, navigation and sensing systems. 

V2X-Based Localization in Underground Tunnels: The Sydney Harbour Tunnel Case Study

This presentation covers a trial aimed at providing a localization solution for vehicles operating within an underground tunnel using a commercial V2X system. It will walk through the entire process, including the installation of equipment in both the tunnel and the vehicle, the implementation of ground-truth positioning systems for performance evaluation, and the testing and validation activities conducted throughout the project.

Speaker biography: 

Stephany Berrío is a Research Fellow at the Australian Centre for Robotics (ACFR), University of Sydney. Her work focuses on connected and automated vehicles, intelligent transportation systems, and vehicle localization technologies. Her research bridges the gap between emerging mobility technologies and practical transportation applications. 

An excursus on uncertainty handling in machine perception

The talk will feature an excursus about handling uncertainty in robot vision tasks, starting from stereo reconstruction and non-normality, passing through monocular object localization, VSLAM and ANN approaches. The thesis is uncertainty gives an edge every time the problem at hand includes different uncertainties, whether they differ qualitatively (different PDFs) or quantitatively (different variances).

Speaker biography:

Domenico G. Sorrenti obtained a Maturità classica (High School with literature and philosophy orientation) at liceo ginnasio statale G. Carducci, Milano, in July 1981. A Laurea (master) in Electronic Engineering, from Politecnico di Milano, in February 1989, and a Ph.D. in Computer and Control Engineering from Politecnico di Milano, in December 1992. He then held research assistant and associate positions at Politecnico di Milano and King’s College London. He then won a tenure assistant professor position at Università di Milano. Currently he is associate professor in computer engineering with Università degli Studi di Milano - Bicocca. He has been the reference professor for the introductory “Computer Architecture” course for the laurea (bachelor) programme in Computer Science. He now teaches “Robotics” and “Embedded Systems” for the same programme, and “Computer and Robot Vision” for the laurea magistrale (master) in Computer Science and the Artificial Intelligence for Science and Technology programmes. He has also been occasionally teaching “Bayesian filtering” for the Ph.D. programme in Computer Science. Research interests: machine perception for autonomous robotics (3D reconstruction, SLAM, self-localization, multi-object tracking, etc.), and video-surveillance. Fundings: european programmes, national programmes, Lombardy region, and private companies. 

When navigation integrity meets AI  

Integrity is a quantitative metric of navigation reliability, crucial for ensuring the safety of automated systems in aviation. It has been an essential factor for certifying navigation systems for decades. In recent years, artificial intelligence (AI) technologies have shown rapid advancements, demonstrating outstanding performance in areas such as perception and vision-based navigation. However, concerns regarding the explainability and generalization capabilities of AI pose challenges for its application in safety-critical, high-integrity navigation systems that rely on multiple sensors. Despite these challenges, AI also holds potential to enhance navigation safety. This presentation explores this paradox and discusses potential strategies to leverage AI in navigation while maintaining quantifiable integrity.

Speaker biography:

Dr. Chen Zhu is a senior research fellow and the head of Visual and Terrestrial Augmentation research group at the Institute of Communications and Navigation, German Aerospace Center (DLR). He received his Ph.D. degree (Dr.-Ing.) and Master’s degree (M.Sc.) from Technical University of Munich, Germany and his Bachelor’s degree from Tsinghua University, Beijing, China. He is interested in the research fields of visual navigation, multi-sensor fusion, and robotic swarm navigation, currently focusing on the system integrity.