IEEE Intelligent Vehicles Symposium 2023


Workshop at IV 2023


Anchorage, Alaska, USA


June, 4th 2023

IV 2023: Third Workshop on Autonomy@Scale

The IEEE Intelligent Transportation Systems Society (ITSS) advances the theoretical, experimental, and operational aspects of intelligent transportation systems, including autonomous driving. The Intelligent Vehicles Symposium (IV 2023) is a premier forum sponsored by the IEEE ITSS in its 34th edition of the conference this year.

This workshop is part of IV 2023 and a follow-up to previous workshops held at IV 2021 and 2022.

Registration is now open! Register here.
IEEE ITSS members will receive a free workshops day only registration!


[05/2023] The agenda for our workshop is online!

[04/2023] Speaker announcement: Exciting news, the lineup of speakers has been finalized. Check it out below.

[04/2023] Three papers have been accepted to our workshop. Check it out below.

[01/2023] Four weeks left to submit your papers!

[12/2022] The paper submissions for the workshop are open. Check out the paper submission details here.
February 01, 2023: Paper Submission Deadline
March 30, 2023: Paper Notification of Acceptance
April 22, 2023: Final Paper Submission Deadline

[10/2022] The third edition of our workshop "Autonomy@Scale" will be held at the IEEE Intelligent Vehicles Symposium in Alaska next year


Aim of Scope

Highly automated vehicles are an integral part of everyday traffic and will be equipped with further autonomy in the future. The world is dynamically evolving which creates  new domains for highly automated vehicles to understand. Besides environmental changes, the constant developments in the field of hardware, software, and development states, pose great challenges. Up until now, AI modules in autonomous driving applications are only scalable to a limited extent. The reason lies in the training strategies applied, in which algorithms have to be trained for each new domain. This results in enormous development costs.

This workshop addresses the need for new training methods for AI systems and research. Disruptive methods of "effective" machine learning can enable a more efficient and unrestricted use of AI and thus “scaling of autonomy” in new domains. With this approach, automated vehicles can access new markets faster and respond agile to new demands.

Invited Speakers

Holger Caesar

Assistant Professor at TU Delft, The Netherlands

Current AV techniques do not scale. To truly bring AVs to all parts of the globe we need unconventional methods. In this talk I will present novel approaches for scalable AVs. First I will discuss how we autolabeled the nuPlan dataset for ML-based planning. Then I discuss a warm start technique called BASAL for active learning for lidar segmentation. Finally I will discuss the HORUS dataset that enables us to study drone-vehicle communication for improved safety.

Oliver Bringmann

Professor at the University of Tübingen, Germany

The recent breakthroughs in using deep neural networks for a large variety of machine learning applications have been strongly influenced by the availability of high performance computing platforms. As a result, today's pre-production self-driving vehicles require high-performance central computing platforms with an electrical power consumption of more than 4000 W. There is an urgent need for a holistic AI system hardware/software codesign approach that enables optimized deployment of multiple machine learning workloads on AI hardware accelerators with minimized energy and resource demand. This talk will discuss techniques for optimized deployment and hardware/software codesign for commercial off-the-shelf and custom accelerator architectures.

Alina Roitberg

Postdoctoral Researcher at Karlsruhe Institute of Technology, Germany

This talk will explore recent advances in video-based driver observation techniques aimed at creating adaptable, data-efficient, and uncertainty-aware solutions for in-vehicle monitoring. Topics covered will include: (1) an overview state-of-the-art  methods and  public datasets for driver activity analysis  (2) the importance of adaptability in driver observation systems to cater to new situations (environments, vehicle types, driver behaviours) as well as strategies for addressing such open world tasks, and (3) incorporating uncertainty-aware approaches, vital for robust and safe decision-making. The talk will conclude with a discussion of future research directions and the potential applications of this technology, such as improving driver safety and improving the overall driving experience.

Yin Zhou

Research Scientist and Senior Manager at Waymo, USA

As robotaxi enters the commercialization phase, one of our main areas of focus is perception generalization, with respect to novel moving objects, or changing conditions (e.g., rain) in the wild. In this talk, I will present our pilot explorations along this line of research.

Accepted Papers

Manuel Schwonberg, Hanno Gottschalk, Nico Schmidt, and Fadoua El Bouazati; "Augmentation-Based Domain Generalization for Semantic Segmentation"

Lukas Chrpa and Mauro Vallati; "Centralised Vehicle Routing for Optimising Urban Traffic: A Scalability Perspective"

Julian Schmidt, Thomas Monninger, Julian Jordan, and Klaus Dietmayer; "LMR: Lane Distance-Based Metric for Trajectory Prediction"

Workshop Organizers

Mercedes-Benz AG


University of Reutlingen


ZF Group