Automated Mobility in Mixed Traffic

Motivation and Aim: 

The integration of automated vehicles (AVs) into existing transportation systems is transforming mobility and introducing unprecedented challenges and opportunities in mixed-traffic environments. In such settings, AVs with varying automation levels coexist with human-driven vehicles (HDVs) and vulnerable road users (e.g., cyclists and pedestrians). While AVs rely on algorithmic perception, planning, and control, human road users act based on experience, reasoning, intuition, risk perception, and social cues. These fundamentally different decision-making mechanisms give rise to complex interaction patterns shaped by human behavior, expectations, and adaptation processes.

As mixed-traffic systems evolve, it becomes essential to understand not only the technical performance of AI-driven systems but also how they interact with, influence, and adapt to human behavior. These dynamic interactions may significantly affect traffic safety, efficiency, energy consumption, and societal acceptance, yet remain difficult to analyze and predict.

Data-driven, empirical, model-based, and simulation-based approaches empowered by advanced AI tools are critical for studying these dynamics. However, challenges persist, including limited model generalization, discrepancies between simulation and real-world behavior, scarcity of high-quality mixed-traffic datasets, and insufficient integration between AI system design and behavioral insights.

By bringing together interdisciplinary expertise, this workshop aims to share state-of-the-art findings, foster in-depth discussion, and explore solutions to advance mixed-traffic research toward robust, innovative, and societally responsive mobility systems.

Building upon the success and experience of previous versions of the workshops at ITSC 2025, ITSC 2024, and ITSC 2023, this fourth edition further pushes forward the research for automated mobility in mixed traffic by: 

• Providing a unique opportunity for knowledge sharing by gathering together notable researchers in the domain and experts from the leading data collection and vehicle automation companies;

• Showcasing the available emerging datasets, their formats, and structure, and discussing their limitations, and challenges for the current research;

• Showcasing and validating state-of-the-art modelling methods and assumptions, with mixed traffic flow datasets; 

• Identifying current research gaps and future research directions, as well as the opportunities for creating synergy between data-driven and theory-driven research;

• Presenting the new IEEE ITSS Technical Committee with its community website for sharing relevant resources (open-sourced datasets, simulation tools and platforms, and pertinent publications).

Participants of this workshop will have the opportunity to communicate with other researchers and experts face-to-face. The goals are to share best practices, discuss common problems that have not been addressed, and gain insights on future research directions, so as to stay ahead of the curve. Additionally, a set of relevant research resources, e.g., open-sourced datasets with detailed summaries, simulation platforms and tools, relevant publication list, will be shared with the participants after the workshop.

Topics of Interest: 

Interested researchers are invited to present their works on the following relevant topics, including but not limited to:

1) Data & Simulation:

·   Mixed traffic datasets, open data practices and open-source platforms.

·   Empirical studies, field tests, and simulation models.

·   Mixed traffic state prediction and system modeling (long/medium/short term).

·   Behavioral and interaction modeling in mixed traffic.

2) Human Interaction:

·   Human factors, trust and user perception of automated systems.

·       Human-AI co-adaptation and meaningful human involvement in mixed traffic.

·       Ethical considerations in mixed autonomy systems

3) Advanced AI in Automated Driving:

·       AI methods in mixed traffic research.

·       LLMs and VLMs applied to automated driving.

·       Sensing, perception, planning and control in automated driving.

4) Safety & Robustness:

·       Model robustness and generalization.

·       Safety assurance and uncertainty quantification.

·       Explainable and trustworthy AI in safety-critical environments.

5) System Impacts:

·       Impact evaluation methods of mixed traffic.

·       Empirical evaluation across automation levels.

·       Traffic flow safety, efficiency, and energy performance in mixed traffic.

·       Societal and policy implications of mixed autonomy. 

Agenda

Agenda at a glance: TBD

Speakers’ Topic and Bio

• Title: Taming noisy point clouds generation: how to balance physics and foundation models

Valentina Donzella, Professor, SEMS Research Excellence Framework Research Culture Lead, AESIN Sensor Lead, School of Engineering and Materials Science, Queen Mary University of London

• Abstract: 

LiDAR is a key sensing technology to enable safe and robust automated mobility in mixed traffic. However, LiDAR data degrades remarkably due to adverse environmental conditions, among which fog is a critical and common one. In order to enable safe LiDAR based perception even under foggy conditions, there is the need of collecting a huge amount of LIDAR data in these conditions, however it is difficult to achieve enough controllability and variability in real-world datasets. In this context, high fidelity simulation of point clouds, systematically degraded due to different fog intensities can play a key role to enablebio the design of robust perception. In this talk we will explore point cloud data augmentation, starting from validated physics based noise models, to more innovative techniques deploying a novel physics-grounded Vision Foundation Model (VFM) framework.

• • Speaker’s Bio:

Valentina Donzella is currently a Professor and the Head of the SPRING (Sensing and Perception for Intelligent Systems) Group, at Queen Mary University of London, U.K. Prof Donzella is the PI on several funded projects on automotive perception sensors, including the EU Horizon ROADVIEW.  She was previously awarded a Royal Academy of Engineering Industrial Fellowship on camera sensors.She is first author, coauthor, and last author of several journal articles on top-tier sensors and intelligent systems' journals. Her research interests include perception sensors for intelligent systems, machine learning, noise modelling, data quality, LiDAR, intelligent vehicles, integrated optical sensors, sensor fusion, and silicon photonics.

Prof. Donzella was the chair of the IEEE ITSS ICVES conference (2025), Associate Editor of the IEEE T-ITS, and is the founder and chair of the IEEE ITSS UK & Ireland chapter. She is a Full College Member of EPSRC and a Senior Fellow of the Higher Education Academy.

• From the Lab to the Street – Open Source Software for Interactive Urban Scenario Driving

Johannes Betz, Professor, Head of the Autonomous Vehicle Systems Lab, Technical University of Munich (TUM), Germany

• Abstract: 

In this talk we will present an open-source autonomous driving software stack designed to enable interactive urban scenario driving, bridging the gap between controlled laboratory research and real-world deployment. Built upon Autoware, the system integrates perception, localization, planning, and control modules into a modular and extensible architecture suitable for rapid experimentation and validation. The approach is demonstrated on the TUM EDGAR vehicle platform, which serves as a full-scale testbed for evaluating interactive behaviors in complex urban environments. Experimental results highlight the robustness, flexibility, and reproducibility of open-source software in advancing safe and scalable autonomous urban driving.

• • Speaker’s Bio:

Johannes studied automotive engineering at Coburg University of Applied Sciences (B. Eng., 2013) and at the University of Bayreuth (M. Sc., 2013) with a focus on electric drive systems and software development. From 2013 to 2018, Johannes was a research assistant at the Technical University of Munich where he received his Dr.-Ing. degree in 2019 on the topic of "Evaluation of an intelligent fleet dispatching for mixed vehicle fleets". From 2018-2020 he was a postdoc at the Department of Automotive Engineering at TUM where he founded the TUM Autonomous Motorsport Team, which successfully participated in the autonomous racing series Roborace and Indy Autonomous Challenge. From 2020 to 2022, he was a postdoctoral fellow at the University of Pennsylvania, USA, where he worked in the xLab for Safe Autonomous Systems. In 2023, he was appointed as Rudolf Mößbauer Professor at the Technical University of Munich where he holds the Autonomous Vehicle Systems Professorship as part of the Department of Mobility Systems.

• Understanding and Modeling Collective Behavior in Autonomous Vehicles and Bicycles

Michail A. Makridis, Deputy Director of the Traffic Engineering and Control group, ETH Zürich, Switzerland

• Abstract: 

This talk puts the spotlight on how emerging traffic flow dynamics can be understood and monitored with increasing deployment of autonomous vehicles (AVs), and how similar principles apply to large-scale bicycle flows. First, the Platoon Fundamental Diagram (PFD) will be introduced and a discussion on its implications for AV traffic, supported by recent empirical evidence will follow. Then, the link between car and bicycle traffic will be presented with key results from the BikeZ project, including insights from experiments, the development of the bicycle PFD (bPFD), and advances in microscopic movement modeling and simulation.

• • Speaker’s Bio:

Dr. Michail A. Makridis is the Deputy Director of the Traffic Engineering and Control group at ETH Zürich, Switzerland. Previously, he led the Transport and Traffic Engineering group at the Zurich University of Applied Sciences and was the scientific lead for the Traffic Modeling Group at the Joint Research Centre (JRC) of the European Commission (EC). He holds a Ph.D. in Computer Vision from Democritus University of Thrace, Greece. His research focuses on traffic flow, management, and control for Intelligent Transportation Systems involving Connected and Automated Vehicles and active mobility. His work emphasizes data-driven, physics-informed modeling and AI to enhance sustainability, traffic efficiency, antifragile operations, and equitable transport networks. In 2022, he received the JRC Annual Award for Excellence in Research from the EC. He is an Associate Editor for the IEEE Open Journal of Intelligent Transportation Systems and serves on various scientific committees. He is a founding member of Antifragility Science working group and member of the steering committee of RERITE working group.

• Reinforcement Learning and Control Barrier Functions for Connected and Automated Vehicles

Maria Laura Delle Monache, Assistant Professor, Department of Civil and Environmental Engineering and the Institute of Transportation Studies, UC Berkeley, USA

• Abstract: 

In this talk, we will present a framework to estimate collision risk in mixed autonomy scenarios when the road is shared between human driven vehicles and autonomous vehicles. The talk will introduce a probabilistic risk assessment framework that uses spatiotemporal occupancy heatmaps and captures uncertainty in predicted interactions.

• • Speaker’s Bio:

Dr. Maria Laura Delle Monache is an Assistant Professor in the Department of Civil and Environmental Engineering and the Institute of Transportation Studies at UC Berkeley. Maria Laura Delle Monache is a member of the Standing Committee on Traffic Flow Theory and Characteristics of the Transportation Research Board. Dr. Delle Monache’s research expertise lies at the intersection of transportation engineering, mathematics, and control theory.

• Topic: TBD

Daniel Work, Chancellor Faculty Fellow, Professor in Civil and Environmental Engineering, Computer Science, Vanderbilt University, USA

• Abstract: 

TBD.

• • Speaker’s Bio:

Dr. Dan Work is a Chancellor Faculty Fellow and Professor in Civil and Environmental Engineering, Computer Science at Vanderbilt University. Dr. Work pioneered methods for monitoring and controlling road traffic using vehicles, rather than fixed infrastructure, to sense and control road congestion. He experimentally demonstrated that "phantom" traffic jams can be eliminated via control of a small fraction of AVs in the flow.

• Multi-objective control of Automated Vehicles considering as a necessity, not an option

Simeon Calvert, Associate professor, Smart & Automated Driving in the department of Transport & Planning, TU Delft, the Netherlands

• Abstract: 

To follow.

• • Speaker’s Bio:

Dr. Simeon Calvert is associate professor of Smart & Automated Driving in the department of Transport & Planning at the TU Delft. He is director and founder of the Automated Driving & Simulation (ADaS) research lab and co-director of the CityAI-lab for research on urban behaviour using AI. He is also a board member of the Centre for Meaningful Human Control over AI, the interdisciplinary research program for responsible autonomous technology. His research is focussed on the impacts of technology on road traffic through experimentation, conceptualization and simulation.

Resource Repository

The online resource repository for sharing relevant Datasets, Simulation Platforms, and Publications on Automated Mobility in Emerging Mixed Traffic can be accessed at https://qiqiqi.gitbook.io/mixed-traffic and https://github.com/IEEE-ITSS-OpenHub/Resource---Emerging-Mixed-Traffic-of-AV-and-HDV.

If you want to share relevant resources with the research community, please contact the workshop organizers.

Previous Editions 

At IEEE ITSC 2023, 2024 and 2025, the organizers hosted a previous edition of this workshop:  

https://www.itsc2025.mixedtraffic.org/

https://www.itsc2024.mixedtraffic.org/,

https://sites.google.com/view/itsc2023-mixed-traffic.

Acknowledgment

The workshop is supported by the interdisciplinary research community and IEEE ITSS Technical Committees of Automated Mobility in Mixed Traffic.