Invited Speaker

Next-Generation Vehicular Communication Networks: Resilience, Twins, and Artificial Intelligence

About Speaker:

Walid Saad received his Ph.D degree from the University of Oslo, Norway in 2010. He is currently a Professor at the Department of Electrical and Computer Engineering at Virginia Tech, where he leads the Network Science, Wireless, and Security (NEWS) laboratory. His research interests include wireless networks (5G/6G/beyond), machine learning, game theory, security, UAVs, semantic communications, cyber-physical systems, and network science. Dr. Saad is a Fellow of the IEEE. He is also the recipient of the NSF CAREER award in 2013, the AFOSR summer faculty fellowship in 2014, and the Young Investigator Award from the Office of Naval Research (ONR) in 2015. He was the (co-)author of twelve conference best paper awards at IEEE WiOpt in 2009, ICIMP in 2010, IEEE WCNC in 2012, IEEE PIMRC in 2015, IEEE SmartGridComm in 2015, EuCNC in 2017, IEEE GLOBECOM (2018 and 2020), IFIP NTMS in 2019, IEEE ICC (2020 and 2022), and IEEE QCE in 2023. He is the recipient of the 2015 and 2022 Fred W. Ellersick Prize from the IEEE Communications Society, of the IEEE Communications Society Marconi Prize Award in 2023, and of the IEEE Communications Society Award for Advances in Communication in 2023. He was also a co-author of the papers that received the IEEE Communications Society Young Author Best Paper award in 2019, 2021, and 2023. Other recognitions include the 2017 IEEE ComSoc Best Young Professional in Academia award, the 2018 IEEE ComSoc Radio Communications Committee Early Achievement Award, and the 2019 IEEE ComSoc Communication Theory Technical Committee Early Achievement Award. From 2015-2017, Dr. Saad was named the Stephen O. Lane Junior Faculty Fellow at Virginia Tech and, in 2017, he was named College of Engineering Faculty Fellow. He received the Dean’s award for Research Excellence from Virginia Tech in 2019. He was also an IEEE Distinguished Lecturer in 2019-2020. He has been annually listed in the Clarivate Web of Science Highly Cited Researcher List since 2019. He currently serves as an Area Editor for the IEEE Transactions on Communications. He is the Editor-in-Chief for the IEEE Transactions on Machine Learning in Communications and Networking.

Real World Test Labs as an Essential Tool for ADAS Development

Abstract:

Testing and validation are currently the limiting factors in the introduction of novel driver assistance systems and automated driving functions. The use of simulation helps to reduce the number of real test drives. However, due to the complexity of the real world, there are still major challenges in realistic modelling. The definition of critical driving scenarios is particularly important. Currently, two complementary approaches are used to generate scenarios: firstly, sampling from a series of parameterized, so-called logical scenarios and, secondly, by selecting challenging scenarios that have occurred in the real world. Real measurements are essential for both approaches: either for the extraction of parameter distributions or to capture edge cases, which cannot be described by a logical scenario and therefore must be determined in real driving tests. For reasons of realism, such test drives should be carried out on public roads, but critical or risky driving situations cannot be tested in real traffic for safety reasons. This requires special test sites that replicate the public traffic area with its existing infrastructure and enable test drives under protected conditions. The KIT Campus East test site of the “Test Area Autonomous Driving Baden-Württemberg” offers ideal conditions for this. The presentation uses various examples to illustrate possible applications.

Michael Frey (Karlsruhe Institute of Technology) Michael Frey is Deputy Director of the Institute of Vehicle System Technology (FAST) at the Karlsruhe Institute of Technology (KIT) and spokesman of the topic “chassis & body” of KIT Mobility Systems Center. He is a member of the steering committee of the “Test Area Autonomous Driving Baden-Württemberg” and responsible for the workpackage “Vehicles & Services”. Michael is also a member of the core team of “Cluster Electric Mobility South-West”, he is involved in the network “intelligent move” and there heads the working group “validation and approval”. Michael is also engaged in teaching students at KIT and HECTOR School of Engineering & Management, Technology Business School of the KIT. He is responsible for practical exercises (student laboratories) and is supervisor of student teams working on an industrial and research projects. Michael received a Diploma in Mechanical Engineering from (Technical) University of Karlsruhe in 1993. In 2004 he received a doctoral degree in Mechanical Engineering from (Technical) University of Karlsruhe.

V2X Communication Technologies in the Era of End-to-End Autonomous Driving

Abstract:

Autonomous driving technology is undergoing a significant paradigm shift from traditional rule-based systems to integrated End-to-End (E2E) deep learning architectures. This transition necessitates a fundamental rethinking of Vehicle-to-Everything (V2X) communication, as existing V2X standards, primarily designed for rule-based systems, may not fully leverage the capabilities or address the needs of E2E models. This presentation explores the evolution required for V2X technologies in the E2E era. We contrast rule-based and E2E architectures, highlighting the limitations of current V2X approaches like object-level message sharing for E2E systems that benefit from richer data. While intermediate feature sharing via V2X is promising, its practical implementation faces hurdles, notably the heterogeneity of sensors, AI models, and tasks across vehicles. To address these challenges, we introduce a research approach aiming to maximize V2X value through an E2E pipeline encompassing data foundation (Co3SOP dataset for collaborative 3D semantic occupancy), perception adaptation (PHCP framework for heterogeneous collaboration during inference), and decision optimization (PrefDrive integrating LLMs with preference learning). Through these interconnected efforts, we aim to unlock the full potential of V2X communication to enhance the safety, efficiency, and robustness of E2E autonomous driving systems. .

Prof. Manabu Tsukada (The University of Tokyo)

Dr. Manabu Tsukada is currently an associate professor at the Graduate School of Information Science and Technology, the University of Tokyo, Japan. He is also a designated associate professor at the Center for Embedded Computing Systems, Nagoya University, Japan. He received his B.S. and M.S degrees from Keio University, Japan, in 2005 and 2007, respectively. He worked in IMARA Team, Inria, France, during his Ph.D. course and obtained his Ph.D. degree from Centre de Robotique, Mines ParisTech, France, in 2011. During his pre and postdoc research stages, he has participated in a multitude of international projects in the networked ITS area, such as GeoNet, ITSSv6, SCORE@F, CVIS, Nautilus6, or ANEMONE. He served as a board member of the WIDE Project 2014-2022. His research interests are mobility support for the next-generation Internet (IPv6), Internet audio-visual media, and communications for intelligent vehicles .

Smart City Transportation Systems for Connected Vehicles and Collision Prediction

Dhananjay Singh (Penn State University)

Dhananjay Singh is a Teaching Professor at Penn State University, USA, and the Director of the ReSENSE Lab, which develops advanced technologies for smart communities. His research interests include Human-Computer Interaction, Intelligent Vehicles, Applied AI/Data Science, and AI-Powered Internet of Things (IoT). Before joining Penn State, Prof. Singh was a Full Professor at Saint Louis University, USA, and Hankuk University of Foreign Studies, Seoul, South Korea. He has over 15 years of experience in academia and industry, serving as a senior researcher, entrepreneur, department chair, and dean at leading institutions in South Korea. Prof. Singh holds a B.Tech. in Computer Science and Engineering from VBS Purvanchal University, an M.Tech. in IT (Wireless Communication and Computing) from the Indian Institute of Information Technology Allahabad (IIIT-A), and a Ph.D. in Ubiquitous IT from Dongseo University, South Korea.

A Senior Member of IEEE, ACM, and IHCI Society, he is an active researcher and has delivered 100+ invited and keynote talks in his field. He also serves as an editor for PLOS One, EAI Endorsed Transactions on Smart Cities, and a book series editor for Springer’s Blockchain Technologies. Additionally, he has been the Organizing Chair of the IHCI conference and co-editor of the proceedings. Prof. Singh is the author of 25+ patents, 3+ books, 15+ book chapters, 7+ conference proceedings (as editor), 75+ conference papers, and 50+ scholarly journal publications. For his contributions to technology, he received the Bhartiya Ratna Puraskar from the State Government of Uttar Pradesh at the 15th Pravasi Bharatiya Divas Convention in Varanasi, India, in January 2019.

The Role of Generative AI for Smart Connected Mobility

Abstract:

In real-world environments such as smart connected mobility, the complexity of data processing is significantly higher compared to controlled laboratory settings. This complexity arises from the combination of multi-dimensional, multi-modal data, and interactions between various systems and processes. Foundation models (FMs) may be a suitable instrument to analyze such data for predictions that in-turn may fuel the next generation of digital twins (DTs) for obtaining reliable insights for decision-making.

However, validating the scalability and accuracy of FMs in processing vast amounts of data at scale and providing reliable predictions is crucial to making them effective for DTs. Therefore, conducting experiments where FMs face realistic data at scale from relevant use cases is important to ensure that insights extracted from the data are accurate, actionable, and capable of leading to meaningful improvements in products and processes.

Christian Berger (University of Gothenburg)

Dr. Christian Berger is Full Professor at the Department of Computer Science and Engineering at University of Gothenburg, Sweden and received his Ph.D. degree from RWTH Aachen University, Germany in 2010. He coordinated the research project for the self-driving vehicle "Caroline", which participated in the 2007 DARPA Urban Challenge Final – the world's first urban robot race. He also co-led the Chalmers Truck Team during the 2016 Grand Cooperative Driving Challenge (GCDC) and is the leading software architect at Chalmers Revere, the laboratory for automotive-related research. His research expertise is on architecting cloud-enabled cyber-physical systems and event identification in multi-modal, large-scale, time-series datasets to support the engineering of safe and trustworthy AI-enabled systems.

Hybrid autonomous driving enabled by smart mobility digital twin

Kei Sakaguchi (Institute of Science Tokyo)

Prof. Kei Sakaguchi received the M.E. degree in Information Processing from Tokyo Institute of Technology in 1998, and the Ph.D degree in Electrical & Electronics Engineering from Tokyo Institute Technology in 2006. Currently, he is working at Tokyo Institute of Technology in Japan as a Professor, and at the same time he is a Senior Scientist at Fraunhofer HHI in Germany. He received the Outstanding Paper Awards from SDR Forum and IEICE in 2004 and 2005, respectively, and three Best Paper Awards from IEICE Communication Society in 2012, 2013, and 2015. He also received the Tutorial Paper Award from the IEICE Communication Society in 2006. He served as a TPC co-chair in the IEEE 5G Summit in 2016, a General co-chair in the IEEE WDN-5G in 2017, and an Industrial Workshop co-chair in the IEEE Globecom in 2017. His current research interests are in 5G cellular networks, millimeter-wave communications, and wireless energy transmission. He is a fellow of IEICE and a senior member of IEEE.

Kei Sakaguchi (Institute of Science Tokyo)

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