Challenges and Contributions in Cooperative and Automated Vehicles
Henk Nijmeijer, Eindhoven University of Technology, Netherlands
Self-driving Vehicles, Can They Be Safe in Mixed Traffic?
Jonas Sjoberg, Chalmers University of Technology, Sweden
Autonomous Vehicles: Wireless Networking for Cooperative Maneuvering
Alexey Vinel, Karlsruhe Institute of Technology (KIT), Germany
Beyond Supervised Deep Learning for Autonomous Driving
Luis M. Bergasa, University of Alcalá, Spain
Challenges and Contributions in Cooperative and Automated Vehicles
Henk Nijmeijer
Eindhoven University of Technology
Netherlands
http://www.wtb.tue.nl
Brief Bio
Henk Nijmeijer (1955) is a full professor at Eindhoven and chairs the Dynamics and Control group. He is an editor of Communications in Nonlinear Science and Numerical Simulations. He is a fellow of the IEEE since 2000 and was awarded in 1990 the IEE Heaviside premium. He is appointed honorary knight of the ‘golden feedback loop’ (NTNU, Trondheim) in 2011. He was an IFAC Council Member in the period 2011-2017. Per January 2015 he is scientific director of the Dutch Institute of Systems and Control (DISC). He is recipient of the 2015 IEEE Control Systems Technology Award and a member of the Mexican Academy of Sciences. He is Graduate Program director of the TU/e Automotive Systems program. He is an IFAC Fellow since 2019.
Abstract
This presentation focusses on the contributions that have resulted from the Dutch I-Cave program. I-Cave stands for Integrated Cooperative and Automated Vehicles and is an academic research program that aims at a variety of aspects emerging from the developmentment of a full size automated vehicle. I will discuss general aspects regarding computer vision and (radar) localization, software architecture, human machine interaction aspects, demonstrator vehicles, and last but not least control aspects for automated and cooperative vehicles. A basic question in this regard is to what extend software architecture of a cooperative vehicle can be one to one used in an autonomous vehicle or not. I will also discuss recent experiments for cooperative adaptive cruise control (C-ACC) using cellular networking technology (G5).
H.Nijmeijer & T.J.van der Sande The Future of Moving Forward, I-CAVE, 2021
Self-driving Vehicles, Can They Be Safe in Mixed Traffic?
Jonas Sjoberg
Chalmers University of Technology
Sweden
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Brief Bio
Jonas Sjöberg is the Head of the Mechatronic research group at Electrical engineering department at Chalmers University, Göteborg, Sweden. Jonas Sjöberg received his master degree from Uppsala University 1989 and the degree of doctor in engineering (PhD) in 1995 from Linköping University, Sweden. After Post-Docs at ETH Zurich, he became Assistant Professor at Chalmers, and after research visits at TU Wien, and at Technion in Haifa, he became a Full Professor in 2001.The research group consists of approximately 25 co-workers, Ph.D students, post docs, and senior researchers. Their research concerns model-based methods, simulations, and optimization for system design and control. Applications are, to a large extent within, self-driving vehicles, automotive active safety and electromobility.Jonas Sjöberg was the winner of Volvo Cars technology award 2011. In 2015 he was co-main chair of the FASTzero symposium, and 2016 he was main chair of IEEE Intelligent Vehicles Symposium. Since 2017 he is BOG member of IEEE ITSS and 2018, he was the winner of the Håkan Frisinger award for outstanding achievements in automotive research in the fields of electromobility and self-driving vehicles.
Abstract
The technical problems for making it possible for self-driving vehicles to drive together with human driven vehicles has turned out to be more difficult than expected for some 5-10 years ago. In this talk we will discuss some of the problems that causing this delay and some approaches on how these problems can be handled. From a decision and control perspective we identify the need to predict other road users, and to understand their intentions, so that safe control action ca be taken. To have safe and efficient traffic, uncertainties of the prediction must be small. Approaches how this can be obtained will be discussed, including infrastructure, methods for predicting other road users, and algorithms limiting the uncertainty. Approaches we work on in academic projects, and with industrial partners will be highlighted.
Autonomous Vehicles: Wireless Networking for Cooperative Maneuvering
Alexey Vinel
Karlsruhe Institute of Technology (KIT)
Germany
Brief Bio
Alexey Vinel (1983) is a professor at the Karlsruhe Institute of Technology (KIT), Germany. Previously he was a professor at the University of Passau, Germany. Since 2015, he has been a professor at Halmstad University, Sweden. He received the Ph.D. degree from the Tampere University of Technology, Finland in 2013. He has been the Senior Member of the IEEE since 2012. His areas of interests include wireless communications, vehicular networking, and cooperative autonomous driving.
Abstract
Last year Elon Musk posted in his Twitter that he had realized that self-driving cars are a "hard problem". We believe that enabling communication between the vehicles is an essential necessary step for cracking this problem in context of fully autonomous urban driving. We will share some of our recent research results on autonomous vehicles with a focus on inter-vehicular networking and respective cooperative driving functionalities. Platooning, i.e. an automatic following of wirelessly connected vehicles closely behind each other, will be presented slightly deeper. We will explain the approach of assessing the safety of the platooning functionality by coupling the quality of radio communications to the likelihood of a rear-end collision. We gratefully acknowledge the support from the Swedish Knowledge Foundation (KKS) in the framework of ”Safety of Connected Intelligent Vehicles in Smart Cities – SafeSmart” project (2019–2023), the Swedish Innovation Agency (VINNOVA) in the framework of ”Emergency Vehicle Traffic Light Preemption in Cities – EPIC” project (2020–2022) and the ELLIIT Strategic Research Network.
Beyond Supervised Deep Learning for Autonomous Driving
Luis M. Bergasa
University of Alcalá
Spain
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Brief Bio
Luis M. Bergasa received the MS degree in Electrical Engineering in 1995 from the Technical University of Madrid and the PhD degree in Electrical Engineering in 1999 from the University of Alcalá (UAH), Spain. He is Full Professor at the Department of Electronics of the UAH since 2011.
From 2000 he had different research and teaching positions at the UAH. He was Head of the Department of Electronics (2004-2010), coordinator of the Doctorate program in Electronics (2005-2010) and Director of Knowledge Transfer at the UAH (2014-2018). He is author of more than 280 refereed papers in journals and international conferences. He was recognized as one of the most productive researcher in Intelligent Transportation Systems (ITS) field during the period 1996-2014, and as a Distinguished Lecturer of the IEEE Vehicular Technology Society (2019-2021). He received the Institutional Lead Award 2019 from the IEEE ITS Society for the longstanding work of his research group. His research activity has been awarded/recognized with 28 prizes/recognitions related to Robotics and Automotive fields from 2004 to nowadays.
He is Associate Editor of the IEEE Transactions on ITS and habitual reviewer in several journals included in the JCR index. He was Guest Editor of two Special Issues (Sensors and IEEE T ITS), member of the Editorial Board of International Journal of Vehicular Technology (2012-2017) and he have served on Program/Organizing Committees in more than 20 conferences. He was Research Visitor at the Computer Vision Research Group of the Trinity College in Dublin (Irland) in 1998, Visiting Scholar at the Toyota Technological Institute at Chicago (USA) in 2013, and at the OPTIMAL Center Northwestern Polytechnic University (China) in 2017. He was co-founder of Vision Safety Technologies Ltd, a spin-off company established to commercialize computer vision systems for road infrastructure inspection (2009-2016). His research interests include driver behaviors and scene understanding using Computer Vision and Deep Learning Techniques for autonomous vehicles applications.
Abstract
Autonomous driving is one of the most exciting engineering fields of our era. The benefits that self-driving cars will have in our society are still unmeasurable, while the associated goals are also growing increasingly complex and challenging. In recent years, fully supervised Deep Learning (DL) algorithms have seen an unprecedented boom in this field and are progressively being introduced into autonomous vehicle navigation architectures. However, compared to classical methods, supervised DL-based techniques face scalability issues as they require huge amounts of labeled data and are unable to generalize to multiple domains. These issues represent a great barrier for the practical application of DL techniques in this field.
In this talk, we will revise the DL developments made by our Research Lab for our Autonomous Vehicle prototype surrounding alternatives to fully supervised learning in the DL context (synthetic data, transfer learning, reinforcement learning, etc.). In addition, we will present our thoughts on the future of research in this field with the purpose of solving the challenging remaining problems.