University of Alcalá, Spain

Miguel Angel Sotelo received the degree in Electrical Engineering in 1996 from the Technical University of Madrid, the Ph.D. degree in Electrical Engineering in 2001 from the University of Alcala, Spain, and the Master in Business Administration (MBA) from the European Business School in 2008. He is currently a Full Professor at the Department of Computer Engineering of the University of Alcala. His research interests include Self-driving cars and Predictive Systems for road users’ behaviours. He is author of more than 320 publications in journals, conferences, and book chapters. He is in the top 1% of the most influential researchers worldwide according to the ranking elaborated by the University of Stanford. Prof. Sotelo has been the Principal Investigator of 54 Research Projects and has supervised ~1.300 person-months in International, National, and Industrial projects, raising substantial funding from the European Commission, Spanish Funding Agencies, and Industry. Prof. Sotelo has presented 40 invited keynotes in international conferences, workshops, and seminars in the USA, Canada, Australia, New Zealand, India, Japan, China, Germany, France, Sweden, Italy, Spain, The Netherlands, Portugal, Romania, Czech Republic, Egypt, Morocco, Argentina, Columbia, Mexico, Panama, and Nicaragua (an average of 5 invited keynotes per year in the last 4 years). He has been a member of PhD International Juries at the University of Chalmers (Sweden), Universities of Coimbra and Aveiro (Portugal), Université de Compiègne and Mines Paris Tech (France), Technical University of Delft (The Netherlands), Karlsruhe Institute of Technology (Germany), Technical University of Graz (Austria), and University of Sydney (Australia). Miguel Angel Sotelo has served as Project Evaluator, Rapporteur, and Reviewer for the European Commission in the field of ICT for Intelligent Vehicles and Cooperative Systems in FP6 and FP7. He served as Editor-in-Chief of the Intelligent Transportation Systems Society Newsletter (2013), Editor-in-Chief of the IEEE Intelligent Transportation Systems Magazine (2014-2016), Associate Editor of IEEE Transactions on Intelligent Transportation Systems (2008-2014), member of the Steering Committee of the IEEE Transactions on Intelligent Vehicles (2015-2018), and a member of the Editorial Board of The Open Transportation Journal (2006-2015). He has served as General Chair of the 2012 IEEE Intelligent Vehicles Symposium (IV’2012) that was held in Alcala de Henares (Spain) in June 2012. In addition, Prof. Sotelo has served as Program Chair and member of the International Program Committee in more than 50 IEEE-sponsored conferences. He has been recipient of the Best Research Award in the domain of Automotive and Vehicle Applications in Spain in 2002 and 2009, and the 3M Foundation Awards in the category of eSafety in 2004 and 2009. He was recipient of the 2010 Outstanding Editorial Service Award for the IEEE Transactions on Intelligent Transportation Systems, the IEEE ITSS Outstanding Application Award in 2013, the Prize to the Best Team with Full Automation in GCDC 2016, and the IEEE ITS Outstanding Research Award in 2022. He was President of the IEEE Intelligent Transportation Systems Society (2018-2019). He is a Fellow of the IEEE and the AAIA (Asia-Pacific Artificial Intelligence Association).

Speech Title: Prediction of Behaviour in Autonomous Driving - The Use of Context as a key for Explainability

Abstract: Self-driving cars have experienced a booming development in the latest years, having achieved a large degree of maturity. Their scene recognition capabilities have improved in an impressive manner, especially thanks to the development of Deep Learning techniques and the availability of immense amounts of data contained in well-organized public datasets. But still, self-driving cars exhibit limited ability to deal with certain types of situations that become natural to human drivers, such as entering a congested round-about, predicting the presence of occluded pedestrians at cross-walks, dealing with cyclists, or giving way to a vehicle that is aggressively merging onto the highway from a ramp lane. All these tasks require the development of advanced prediction capabilities that rely on contextual reasoning in order to anticipate the most likely behaviours and trajectories for all traffic agents around the ego-car. In addition, predicting and understanding the behaviour of other road users opens the gate to the development of trustworthy and friendly-interacting autonomous vehicles. This talk will present some innovative solutions for explainable road users’ behaviour prediction in the context of autonomous driving as well as the way these predictions can be leveraged to implement optimal interactions between autonomous vehicles and road users. Latest results achieved in the framework of the EU-funded BRAVE and HEIDI projects will be discussed as a corner stone to shed light on the path forward in this field.

University of Minnesota, USA

Dr. Xinyu Cao is a professor at the Humphrey School of Public Affairs, University of Minnesota, Twin Cities and a visiting scholar of Chang’an University.  He specializes in land use and transportation interaction and planning for quality of life.  He has published more than 140 peer-reviewed papers and edited four books.  Dr. Cao is internationally well-known for his research on residential self-selection in the relationships between the built environment and travel behavior.  He is currently leading the area of machine learning applications in land use and transportation research.  Dr. Cao is the Co-Editor-in-Chief of Transportation Research Part D and an associate editor of Transport Policy and Journal of Planning Education and Research.  Dr. Cao received his degrees from the University of California, Davis and Tsinghua University.

Speech Title: Enlightening the Relationship between Land Use and Travel through Machine Learning

Abstract: As a conventional approach to uncovering the relationships between variables, data modeling usually requires a priori assumption. However, if this assumption does not hold true, data modeling may yield questionable findings and theories. This presentation highlights the power of machine learning approaches in improving our understanding of the relationship between variables. Using examples in the field of land use and travel behavior, the presenter demonstrates that these approaches can (1) correct wrong conclusions from data modeling; (2) identify seemingly important but impractical land use interventions; and (3) discover interaction effects between variables without prior knowledge.

University of Plymouth, UK

Professor Long-yuan Li is Professor of Structural Engineering in School of Engineering, Computing and Mathematics at University of Plymouth, UK. Professor Li’s research interests cover the fields of mechanics of materials, durability, reliability, and fire safety of RC structures. He has published over 200 technical papers in SCI journals with Scopus h-index 45. Professor Li is the Fellow of the Alexander von Humboldt Foundation (Germany), Fellow of the UK Higher Education Academy, and Fellow of the Institution of Structural Engineers (UK). He is the Member of EPSRC Peer Review College, Member of UK Society for Computational Mechanics in Engineering, Member of UK Concrete Society, and Member of International Society for Interaction of Mechanics and Mathematics. Currently, Prof Li is the editor of “Construction and Building Materials” journal, member of editorial boards of “Cement and Concrete Composites”, “Magazine of Concrete Research”, “Journal of Marine Engineering & Technology”, etc. 6 international journals.

Speech Title: Durability problems of infrastructure in transportation engineering

Abstract: Durability is a critical factor in ensuring the long-term performance and safety of infrastructure in transportation engineering. This presentation addresses the durability challenges faced by key structural components of transportation systems, including road pavements, tunnels, bridges, and railway infrastructure. It highlights how geopolymer materials, derived from industrial by-products, can be applied to road pavements to mitigate the effects of climate change and increasing traffic loads. Additionally, these materials can enhance tunnel safety under extreme conditions by improving the structural integrity and fire resistance of tunnel linings. For bridges and railway infrastructure, the presentation demonstrates the use of electrochemical methods to prevent corrosion of reinforcing steel in concrete, thereby improving the overall durability of concrete structures. This presentation offers an in-depth exploration of the latest advancements in materials and technologies aimed at enhancing the durability of transportation infrastructure, ultimately contributing to safer and more sustainable transportation networks.

University of Waterloo, Canada

Xianguo Li is a Mechanical and Mechatronics Engineering Professor at the University of Waterloo.

Professor Li's main research interests and activities are in the area of thermal fluid/science, including energy systems and energy storage, various energy conversion devices, propulsion and power generation systems, aerosol generation and applications, and transportation fuel cell and battery systems. These research projects involve thermodynamics, fluid dynamics, hydrodynamic stability, multiphase flow, heat and mass transfer, liquid atomization and sprays, combustion, power generation and propulsion systems.

Professor Li is the Founding Editor-in-Chief of the International Journal of Green Energy, which established the International Green Energy Conference series and launched the annual review series Progress in Green Energy. He is also the Field Chief Editor, Frontiers in Thermal Engineering. He is currently serving on the editorial board of dozens of international scientific/technical journals, book series on fuel cells and energy systems, as well as an encyclopaedia on Energy Engineering and Technology.

Professor Li is a fellow of Canadian Academy of Engineering (FCAE), a fellow of the Engineering Institute of Canada (FEIC) and a fellow of the Canadian Society for Mechanical Engineering (CSME), and serves as VP Technical Program for CSME. Previously he served as the CSME Division Chair for the Advanced Energy Systems technical division. He also currently serves as the President of the International Association for Green Energy and President of the Fuel Cell Division, International Association for Hydrogen Energy and established the World Fuel Cell Conference series.