STADT:up - Perspectives of Urban Mobility
Solutions and Technologies for Automated Driving in Town: an Urban Mobility Project

Below you will find a short summary of the project. For further information please contact the project managers via the contact button on the right side.

Background

In the collaborative project STADT:up, funded by the Federal Ministry for Economic Affairs and Climate Action, 22 partners from industry and research are developing concepts and pilot applications tailored to user needs for integrated automated driving in urban areas. The focus is on the implementation of new, AI-based methods and their demonstration in automated driving systems, especially in complex traffic situations. Special consideration is given to vulnerable road users, complex intersections and automated merging and obstacle avoidance.

Aims

STADT:up aims at integrated, scalable solutions for future urban mobility: The vehicles must also be able to safely master complex inner-city traffic scenarios. With regard to realistic perspectives of future urban mobility, suitable future concepts are developed and requirements are derived based on the needs of the users.

In the STADT:up project, the Institute Ergonomics and Human Factors is researching sustainable traffic concepts for automated mobility in the city in the sub-project “Perspectives of urban mobility”. Of central importance is the question of how the mixed traffic of pedestrians, cyclists, privately or jointly used vehicles and public transport will develop in the future.

Method

To integrate the perspectives of urban mobility, interviews and workshops are conducted with stakeholders of the city of Darmstadt and the surrounding area (e.g. urban planners, politicians, transport companies, citizens) to understand the ideas of future mobility from different perspectives. The identified mobility needs are incorporated into the definition of specific scenarios for the implementation of mobility solutions involving automated vehicles. These scenarios are then analysed in experiments with a focus on user acceptance and user decisions.