What we do

The research group Cooperative Autonomous Systems conducts applied research on coordination, cooperation, and communication between autonomous or highly automated vehicles, vulnerable road users, and transportation infrastructure in complex environments. We focus on the design and evaluation of vehicular communication (V2X) protocols, cooperative driving functions such as platooning, and new concepts for road transport coordination within Cooperative Intelligent Transport Systems (C-ITS). Our work combines real-world lab experiments with simulation-based methods to investigate future mobility from a transdisciplinary perspective. 

A central part of our research is to explore how different mobility actors, especially vulnerable road users, can benefit from V2X technology. We are interested in extending the V2X paradigm to emerging forms of mobility, including e-bikes and social robots, with the goal of improving safety and inclusiveness in public spaces. 

In the project V2X4Robot, we integrate formal specifications, vehicular communications, and human-robot interaction to enable a social robot to support pedestrians when interacting with automated vehicles. 

In the EU project CulturalRoad, we contribute to the fair and culturally sensitive deployment of Cooperative, Connected, and Automated Mobility (CCAM) services. By taking into account cultural and geographical diversity in CCAM planning, we aim to improve societal acceptance and promote a mobility future that is safe, inclusive, and sustainable. 

The project C2CBridge 2 investigates new approaches to connect rural and urban areas using autonomous electric mobility solutions. We analyze the impact of platoon driving on surrounding traffic and passenger experience by combining traffic simulations with user studies. 

In addition to our work on coordination and communication, we also address environmental aspects of mobility. The project TyreRoadNoise focuses on reducing tyre-road-generated emissions (RFGE). To achieve this, we are creating a database of real-world traffic conditions and developing AI-based prediction models that help bridge the gap between physical models and observed noise behavior. 

To support safe and scalable development, we make use of cyber-physical systems. These systems allow us to conduct controlled experiments in environments with reduced risk, complementing our real-world studies.