Automatic relocation of link related data in an updated road map

For a rising number of ITS applications, location information obtained by the processing of sensor data is related to the links of a specific digital road map. Such maps are available from different vendors like Here, TomTom/TeleAtlas and OSM. They are created with different philosophies, resulting in significant differences in the geometry and the topology of the road networks. If a map needs to be updated to a new release, the user faces the problem that a relocation of any annotated location data, i.e. a proper mapping of these locations from the old to the new map becomes necessary. For this reason, DLR developed a new prototypic software application called DataRelocator@Map2Map. It enables the automatic relocation of location data between the two maps. Using this new tool, an almost fully automatic relocation is possible and thus the cost of service failures related to the map update can be avoided

Traffic Information Systems for Smart Mobility as part of Smart Cities

The unlimited and unrestricted mobility of people and goods in urban areas is one of the key factors for economic and social development of the city. Today with the availability of smart technologies and various intelligent transportation and telematics solutions the Smart Mobility as part of a Smart City is possible to maintain the mobility ecosystem in the city. But to make the urban mobility smart by assuring the sustainability, safety, low emission and comfort in urban transport new mobility concepts are required. This paper introduces an architecture for smart mobility systems and describes in general the requirements of such systems. The focus for this contribution is on the traffic information and management systems for public, private and shared mobility. In addition to the traffic information and data sources, this paper also deals with social media as new traffic data source as well as the environment data. Furthermore, some use cases selected from different national and international ITS projects are also presented

Utilizing historical and current travel times based on floating car data for management of an express truck fleet

During the last nine years, a couple of prototype ITS applications based on Floating Car Data (FCD) of taxi fleets have been developed at German Aerospace Center (DLR). A core application is a route guidance and monitoring system based on current and historical road segment travel times. Recently, it has been extended for use in the German funded project SmartTruck, run by a consortium consisting of the logistics key player DHL, DLR and the German Research Center for Artificial Intelligence (DFKI). An important aim of the project was the use of historical and current traffic information for energy-efficient, optimized offline planning and dynamic re-planning of the tours of DHL express trucks in Berlin, Germany. This paper discusses the architecture of the SmartTruck system and the methodology used to generate historic and current road segment travel times from positional data

LiSuM: Design and Development of a Middleware to couple Virtual LISA+ TLS Controller and SUMO Simulation

Traffic signal control logic programs are analyzed and tested in traffic fow simulators before being put into operation in real traffic road intersections. LISA+ is a proprietary software tool used to plan and evaluate complex intersections and the control logic programs created with it can be directly uploaded to real controllers or tested with VISSIM, a proprietary microscopic 3D traffic simulator. On the other hand, Simulation of Urban Mobility (SUMO) is a free and open traffic simulation toolsuite that facilitates simulation of traffic and the evaluation of infrastructure changes as well as policy changes before implementing them on the road. As mentioned above, LISA+ control logic programs can be currently tested only with proprietary software and a free and open alternative, like SUMO, is desirable. To close this gap this paper presents the design and development of a Middleware called LiSuM that enables the communication and interaction between the virtual LISA+ traffic light signal (TLS) controller and SUMO. Furthermore LiSuM provides also a friendly graphical user interface (GUI), which allows managing all aspects related to the interaction between LISA+ and SUMO. The main features of the LiSuM Middleware as well as the provided interfaces are also described. The usage of LiSuM is demonstrated for a single intersection simulated with SUMO and controlled by LISA+

Internet of Things (IoT) for autonomous driving in case of Automated Valet Parking (AVP)

The service development of autonomous driving use cases progressed by the internet of Things (IoT) is introduced in the presentation. In the context of six autonomous driving use cases, which were developed and realised for the European large scale project AUTOPILOT, the focus of the presentation is on the system design and development of the Automated Valet Parking (AVP) use case enabled by the IoT technology to support smart mobility for the future. Finally, the results of the field operational test at a pilot site in the Netherlands (Brainport) are presented

TRAFFIC DATA PLATFORM AS ITS INFRASTRUCTURE FOR INTELLIGENT TRAFFIC DATA MANAGEMENT

Many industrial and research projects in the field of ITS need traffic data for realizing new and innovative applications. However, it is often costly and time-consuming to acquire or to access such data. That is where a complete traffic data platform with standardized access can increase the efficiency of research for current and future ITS projects. For this purpose, the Institute of Transportation Systems of the German Aerospace Center (DLR) is developing a modular and SOA based Traffic Data Platform (TDP) which provides all basic tools for storage, processing, fusion and management of traffic data from various sources. In this context, the TDP is especially designed to support “online” services and information in a most efficient way. Moreover, due to its modularity and extensibility, the platform itself can be used as a framework for testing and developing new methods and algorithms for data fusion and quality estimation, for example. This paper gives a general overview about the TDP and focuses on the technical aspects. The main functionalities, non-functional requirements, and the key players are mentioned as well as the supported data. Finally, a selected use case scenario shows the practical applicability of the TDP

Level of Service (LOS) based Data Fusion to enhance the Quality of Traffic Information

Today, the road side level of service (LOS) information is a commonly used measure for describing the current road network efficiency. It characterizes traffic flow or travel times by a small number of discrete traffic quality classes (e.g. free flow, dense traffic, traffic jam) and is one of the most popular kinds of traffic information in road transportation. In this context, LOS-based information is often obtained from single traffic data sources like floating car data or detector data without combining them systematically. In order to improve the quality of LOS-based traffic information a high level fusion approach is needed which integrates LOS data from various data sources. This contribution suggests a new methodology using a quality-weighted median approach to efficiently and generically fuse these LOS data. It principally handles the common discretization schemes and provides a stochastic algorithm for transforming LOS data of arbitrary scales. The new methodology is described together with a use case for a region in Germany and the technical description of the prototypical LOS data fusion software module as designed and implemented by the Institute of Transportation Systems of the German Aerospace Center (DLR)

Special Interest Session SIS 10: Complex Self Driving Field Operational Tests using evolved IT Infrastructure

Field Operational Tests (FOTs) are being used to evaluate complex safety-relevant functions of self-driving vehicles using sensor Fusion and SLAM technologies supported by evolved digital infrastructure. These FOTs are designed to assess the impact of the next Generation IT infrastructure including cloud and mobile-edge computing, IoT and enhanced connectivity through next generation mobile networks. New types of data from IoT-connected heterogeneous sensors and bigger datasets, provided and managed by complex cloud and mobileedge infrastructure, raise novel challenges. FOTs also play an important role in evaluating new business models and issues such as data privacy and liability that are central to self-driving. In this session, experts will present how Field Operational Tests handle some of These complexities and answer some interesting questions: How far can the current FESTA be used for FOTs? How does big data contribute to self-driving evaluation? How must user experience be considered in self-driving vehicles

Toward Fast and Accurate Map-to-Map Matching of City Street Maps

Frequently, various sources of geographic street-related data are covering the same space. Many geospatial traffic services require interoperability of the different datasets, which can be achieved by road network matching. A prominent use case is map conflation. More recently, the authors have suggested approaches to dynamic location referencing between maps (GIMME) and to automatic relocation of link related data in updated street maps within a framework called Map2Map. In this paper, an update on the recent progress of GIMME and Map2Map is given. Methodologically, path contraction is used to obtain a simplified version of the digital road network. This pre-processed version then augments the original network, and serves as a guide for finding routes covering the entire network, and facilitating the inter-map matching process. Path contraction also helps to reduce the complexity of the core inter-map matching method GIMME without loss in matching quality. On the conceptual side, a general strategy called calibration-preserving pre- or post-processing (C-3PO) is introduced. The aforementioned path contraction and two more post-processing methods used in Map2Map give examples for an implementation of C-3PO. Experimental results demonstrate the effectiveness of the presented approach