United States

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.

VEHITS 2022

Self-driving Vehicles, Can They Be Safe in Mixed Traffic?

The purpose of the International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS) is to bring together engineers, researchers and practitioners interested in the advances and applications in the field of Vehicle Technology and Intelligent Transport Systems.
This conference focuses on innovative applications, tools and platforms in all technology areas such as signal processing, wireless communications, informatics and electronics, related to different kinds of vehicles, including cars, off-road vehicles, trains, ships, underwater vehicles, or flying machines, and the intelligent transportation systems that connect and manage large numbers of vehicles, not only in the context of smart cities but in many other application domains.

The purpose of the International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS) is to bring together engineers, researchers and practitioners interested in the advances and applications in the field of Vehicle Technology and Intelligent Transport Systems.

keynote

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.

Beyond Supervised Deep Learning for Autonomous Driving

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.

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Beyond Supervised Deep Learning for Autonomous Driving

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