To evaluate the impact of new intelligent mobility solutions like automated vehicles or C-ITS, the access to proper data observations in real-world environments is an absolute necessity. The challenges new automated mobility faces in urban areas are manifold and require spatially and temporally extensive data from real world traffic situations and interaction scenarios with other road users. This paper focuses on supporting the shift of our current urban mobility systems – made possible by the emergence and confluence of new transportation technologies like vehicle automation – by providing such real-world mobility data. The data was recorded in the city of Munich, Germany, continuously for several hours a day and several days with all together twelve camera-equipped aerial drones. The aim was to generate a large-scale continuous data set including various interactions between classical human-driven cars and automated vehicles as well as active mobility users with human-driven and automated vehicles. For this purpose, the urban area drone footage covers trajectories for both human-driven and automated vehicles as well as active road users like pedestrians, cyclists, and persons with disabilities. The trajectories were extracted from the video images and merged continuously in time and space across several drone observation areas and subsequent time slots. During the simultaneously running field test, the participating connected and automated vehicles were also made clearly visible to other road users as being automated. This was ensured by large explanatory stickers at the car body and a sensor mounting structure on the roof. The whole data set will be published open source to ensure a perfect global accessibility for scientists and practitioners for further research.     «

To evaluate the impact of new intelligent mobility solutions like automated vehicles or C-ITS, the access to proper data observations in real-world environments is an absolute necessity. The challenges new automated mobility faces in urban areas are manifold and require spatially and temporally extensive data from real world traffic situations and interaction scenarios with other road users. This paper focuses on supporting the shift of our current urban mobility systems – made possible by the...     »