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The TEMPUS test field in the North of Munich: Freeways in blue, urban roads in the City of Munich in red, rural roads in orange and roads in smaller surrounding municipalities in green.
Source publication
This paper presents the goals of the German TEMPUS research project in and around the city of Munich and describes the test field that is being created as part of it. The project will test and evaluate the impact of connected and automated driving in urban and rural environments. For this purpose, a cross-area test field, which includes inner-city...
Contexts in source publication
Context 1
... test field within the TEMPUS project will include urban area in north Munich, a rural area and freeways. The urban area, indicated with red markers in Figure 1, is in the north of Munich covering main arterials and smaller streets. Within the test field, several intersections will be equipped with roadside units for communication and data transmission to vehicles, using state of the art communication standards (LTE V2X and ETSI ITS-G5). ...
Context 2
... extra-urban part of the test field covers the freeways A9, A92 and A99 in the north of Munich, as well as the federal highways B13 and B471, displayed as the blue (freeways), orange (federal highways) and green (municipal roads) streets in Figure 1. The traffic signals along the federal highways will be equipped with roadside units in order to implement a traffic signal phase assistant to adapt the driving strategy and to prioritize emergency vehicles. ...
Citations
... While georeferenced models are required to test real-world scenarios, (fictional) 3D test environments within a local system are sufficient for general simulations. Kutsch et al. [103] present a test field designed for developing connected and automated driving functions. A highly accurate semantically rich 3D representation of roads and the environment are the basis for this project. ...
In addition to geometric accuracy, topological information, appearance and georeferenced data, semantic capabilities are key strengths of digital 3D city models. This provides the foundation for a growing number of use cases, far beyond visualization. While these use cases mostly focused on models of buildings or the terrain so far, the increasing availability of data on roads and other transportation infrastructure opened up a range of emerging use cases in the field of semantic 3D streetspace models. While there are already a number of implemented examples, there is also a potential for new use cases not yet established in the field of 3D city modeling, which benefit from detailed representations of roads and their environment. To ensure clarity in our discussions, we introduce an unambiguous distinction between the terms `application domain’, `use case’, `functionality’ and `software application’. Based on these definitions, use cases are categorized according to their primary application domain and discussed with respect to relevant literature and required functionalities. Furthermore, requirements of functionalities towards semantic 3D streetspace models are determined and evaluated in detail with regard to geometric, semantic, topological, temporal and visual aspects. This article aims to give an overview on use cases in the context of semantic 3D streetspace models and to present requirements of respective functionalities, in order to provide insight for researchers, municipalities, companies, data providers, mapping agencies and other stakeholders interested in creating and using a digital twin of the streetspace.
... The experiment and the dataset are described in more detail in the following chapters. The data set was gathered within the Munich TEMPUS project (Kutsch et al. 2022), a project to prepare the Munich urban and suburban road network for connected and automated driving. ...
Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behavior of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 m continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data, therefore, includes various interactions between different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and the supporting data are available open source for research purposes to ensure global accessibility.
... The experiment and the dataset are described in more detail in the following chapters. The data set was gathered within the Munich TEMPUS project (Kutsch et al. 2022), a project to prepare the Munich urban and suburban road network for connected and automated driving. ...
Currently available trajectory data sets undoubtedly provide valuable insights into traffic events, the behaviour of road users and traffic flow theory, thus enabling a wide range of applications. However, there are still shortcomings that need to be addressed: (i) the continuous temporal recording (ii) of a coherent area covering several intersections (iii) with the detection of all road users, including pedestrians and cyclists. Therefore, this study focuses on the design of a large-scale aerial drone observation in the city of Munich, Germany, as well as the processing steps and the description of the resulting data set. Using twelve camera-equipped, unmanned aerial drones, the observation monitored an inner urban road section with a length of 700 meters continuously for several hours during the afternoon peak hours on two working days. The trajectories of all road users were then extracted from the videos and post-processed in order to obtain a coherent and accurate data set. The resulting trajectories contain the information on the category, dimensions, location, velocity, acceleration and orientation of each road user at each frame, merged continuously in time and space across several drone observation areas and subsequent time slots. The data therefore includes various interactions of different modes of motorized traffic and active mobility users like pedestrians and cyclists. The whole data set and supporting data is available open source for research purposes to ensure global accessibility.
... The RPP research described in this paper is part of the "Test Field Munich -Pilot Test for Urban Automated Road Traffic" project "TEMPUS" [52]. The German Federal Ministry of Transport and Digital Infrastructure provides funding through the project with grant number 01MM20008K. ...
Ride Parcel Pooling (RPP) is the integrated transportation of passengers and parcels. It builds on what's known as on-demand ride-pooling, which dynamically assigns customer trips to fleet vehicles in real time, sharing their rides, and thus aiming to save on driving distances and fleet size compared to on-demand ride-hailing services. In the case of RPP, a additional parcel demand is introduced that is less sensitive to longer travel and waiting times and is less negatively affected by changing vehicles. In this case, parcels are served with a lower priority than passengers and simply ride along with appropriate passenger trips. This makes it possible to achieve a more efficient use of vehicle capacity and further reduce the total distance traveled for passenger and logistics mobility. This research is divided into a conceptual and scenario definition part to define the proposed service and develop possible operational scenarios, a theoretical simulation-based approach to quantify the potential of the proposed RPP service, and a practical field test to investigate its real-world applicability. Agent-based RPP simulations show that the integration of logistics services into a ride-pooling service is possible and can exploit unused system capacity without degrading passenger service. Depending on the assignment strategies, parcels can be served up to a parcel-to-passenger demand ratio of 1:10, while total fleet kilometers can be reduced compared to the status quo, as the additional mileage for logistics service can be fully integrated. In the base scenario of the simulation, approximately 50,000 passengers and 5,000 parcels are transported by a fleet of 600 cars or 1,200 rickshaws. The RPP field test included a mobile phone web app and five bicycle rickshaws offering the RPP service in the Maxvorstadt district in Munich, Germany. Each rickshaw had two passenger seats and additional space for parcels. The service was available daily between 11:00 and 19:00 for one week and was completely free for users. The field test showed that the RPP service is ready to operate today and provided interesting insights into the real-world operational parameters for such a service.
... For this purpose, an automated and connected cycle rickshaw is introduced to provide passenger trips as well as to contribute towards city logistics. The rickshaw was developed as part of the research project TEMPUS (Test Field Munich -Pilot Test for Urban Automated Road Traffic) [3] financed by the German Federal Ministry of Digital and Transport. The automated and connected rickshaw represents a sustainable and efficient solution for developed and for developing countries. ...
This paper presents the implementation and potential use-cases of a new innovative development of a fully connected and automated three-wheeled cycle rickshaw. The rickshaw is used for transporting passengers or logistics parcels or a combination of both. For this purpose, the rickshaw is equipped with an electric power train system including a lithium accumulator as well as actuators for breaking, propulsion and the steering of the front wheel. The environmental sensing is currently realized via three LiDAR sensors mounted at the roof of the rickshaw observing the 360-degree surrounding of the vehicle. Equipped with a state-of-the-art on-board unit for V2X-communication and remote-control access in cases of overstress situations for the trajectory planning and self-driving functionality the rickshaw represents a fully equipped connected and automated vehicle for urban road traffic. The rickshaw shows a substantial potential to increase the productivity, reliability and flexibility of logistics and transport services. A higher degree of automation in logistics and freight transport-within this prototypical implementation realized by the self-driving functionality-allows for a more cost-efficient operation of logistics fleets and businesses. Additionally, less weight due to the absence of a driver and lightweight components also leads to a reduced energy consumption. The developed self-driving rickshaw gives the opportunity to automatize first-and last-mile logistics services as well as passenger transport with reduced costs.
... Another benefit is the availability of such a data set to train further video recognition algorithms and neural networks. The data set was gathered within the Munich TEMPUS project [2], a project to prepare the Munich urban and suburban road network for connected and automated driving. Lots of data sets and research for the implications of connected and automated vehicles on the safety and operation are already available for freeways [3] Therefore this work also contributes to further knowledge about the traffic behavior of such vehicles particular in urban traffic networks. ...
... In addition to that, truck trailers as well as bus trailers (in the city of Munich several high-capacity bus routes operate buses with bus trailers) were also recognized and categorized. The observed road segment also lies in the area of Munich's TEMPUS test bed for connected and automated driving [2], Therefore, also three vehicles with connected and automated driving functions were part of the drone observation data set, mainly focusing on the use cases of automated vehicle platooning (three automated vehicles following a human-driven fourth vehicle) and vehicle interactions with crossing pedestrians at unsignalized locations. In addition to the aforementioned traffic participants the dataset also contains trajectories of an electrified bicycle Rickshaw from the TEMPUS project [18,19], (E-)Cargo-Bikes and persons with a wheelchair. ...
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.
This study focuses on the data gathered and processed by a large-scale aerial drone observation 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 continuous data set including various interactions of different modes like manual and automated cars as well as active mobility users like pedestrians and cyclists. For this purpose, the urban area drone footage covers trajectories for all those traffic participants which were extracted from the video images and are merged continuously in time and space across several drone observation areas and subsequent time slots. The whole data set will be published open source to ensure global accessibility for further research.
