Due to the increased demands of traffic safety applications many parameters of the traffic flow have to be measured by different kind of techniques. The sensors of vehicle sensing and detection are to be inexpensive, currently available and easy to deploy. Therefore, the state-of-the-art safety applications are usually based on mature technologies, such as video cameras, smart dust sensors, and wireless communication technologies. This paper discusses the application of RFID technology for transportation applications. RFID is widely used for different purposes (e.g. cargo logistics, storage management), but is still considered as new technology in the field of safety-related applications. The paper gives an overview about the technology and describes scenarios of using RFID as infrastructure as well as vehicle sensor. The capabilities and limitations of RFID technology is demonstrated through the ghost driver (wrong way driving) detection, which is discussed in detail. In this particular application RFID is used as an infrastructure sensor, multiple readers are connected in a network, thus able to monitor the traffic flow directions in a particular road segment (e.g. in a motorway junction). Additional applications, such as flow classification, road toll control and emergency monitoring are also discussed in the paper.
The vehicles of the conditional, high, and full automation levels have a common unique sensor, the map. The term map has undergone a significant change because the spatial resolution has been increased considerably, the road infrastructure and its neighborhood are represented with higher accuracy in 3D. The development of these vehicles requires enormous efforts, where computer-based techniques, like the simulations, can offer a helping hand. The autonomous simulations will be supported by high-quality map information, which generates interest in the best field data-capturing techniques. The paper provides an overview of the available modern surveying methodologies, then introduces the most preferred data formats – both in physical information storage and in exchanging information content between mapping systems. Some examples are presented to demonstrate the usage of the relevant map-making outputs in automotive simulators.
The development of autonomous vehicles nowadays is attractive, but a resource-intensive procedure. It requires huge time and money efforts. The different carmakers have therefore common struggles of involving cheaper, faster and accurate computer-based tools, among them the simulators. Automotive simulations expect reality information, where the recent data collection techniques have excellent contribution possibilities. Accordingly, the paper has a focus on the use of mobile laser scanning data in supporting automotive simulators. There was created a pilot site around the university campus, which is a road network with very diverse neighborhood. The data acquisition was conducted by a Leica Pegasus Two mobile mapping system. The achieved point clouds and imagery were submitted to extract road axes, road borders, but also lane borders and lane markings. By this evaluation, the OpenDRIVE representation was built, which is directly transferrable into various simulators. Based on the roads' geometric description, a standardized pavement surface model was created in OpenCRG format. CRG is a Curved Regular Grid, containing all surface height information and objects, but also anomalies. The 3D laser point clouds could easily be transformed into voxel models, then these models can be projected onto two vertical roadside grids (ribbons), which are practically an extension to the OpenCRG model. Adequate visualizations demonstrate the obtained results.
The development of automotive technologies requires quite a significant amount of time and money. To accelerate this procedure, the technology of now is strongly based on computer simulations, where the whole vehicle or its parts can be analyzed in a virtual environment. The behavior of cars, especially equipped with new sensors or assistants, requires long testing, where the automotive simulators can play a cardinal role. The precise vehicular tests request accurate environmental models. These new kinds of models are still standardized; one of the pioneer de facto standards is OpenDRIVE. This standard was initially defined to be able to express all elements with all potential parameters required in high precision simulations. The actual research focused on creating a compliant virtual model based on mobile mapping measurements. A Leica Pegasus Two mobile mapping system was applied to capture field data about the selected pilot area, which is the campus of Budapest University of Technology and Economics (BME). The obtained Lidar point cloud was georeferenced; the merged point cloud is tailored to the driven trajectory, and then it has been evaluated manually. The acquired land use map is converted – similarly manually – into basic road geometry elements: straight lane and bended lane segments. These objects are finally compiled into an XML format, which is compliant with the OpenDRIVE standard. The achieved virtual model has been tested in Driving Scenario Designer of Mathworks Matlab; however, it is promptly ready for use in other widely applied automotive simulators.
Abstract. A self-driving vehicle is one of the most expected inventions in the near future. These vehicles are enabled by several technological developments, like artificial intelligence, robust control, vehicular sensors, and high-speed communication. But beyond all these elements, the essential component is the knowledge about reality. Our profession has answered that question with the development of high-definition (abbreviated as HD) maps. Fully automated driving (also called driverless transportation) must be reliable enough to entrust our lives to the car. This fact indicates that the applied technology and the used map must be of high quality. But how can the quality of such a map be expressed? We are looking for the answer in the current paper.Following Carlo Batini’s idea, the general approach is based on the triumvirate of data sources – quality dimensions – life cycle phases. Data sources cover aerial, terrestrial and mobile mapping products with the available highest technological care; furthermore, onboard vehicular sensing extends the corresponding data sets. Lifecycle phases focus on the production (data collection and processing technologies) expanded by conceptualization (pre-production) and data delivery and use (post-production). Quality dimensions are strongly related to the dimensionality of the data; they can be measured by dimension metrics.The first part of the paper summarizes the applied data collection methodologies, emphasizing the output data. This description contains a summary of the processing mechanism – inevitably characterized by quality indicators. The paper aims to give a complete outline for the quality dimensions; we do not limit the resolution and accuracy dimensions, but other significant clusters like completeness or consistency are also discussed. Because the reality changes are enormous in transportation (vehicles, pedestrians, etc., are moving – even at higher speed) and the newly developing HD maps are expected to be live, actuality is a cardinal quality dimension as well. Vehicular technologies like SENSORIS give an excellent option to the equipped vehicles to download and use maps from the cloud and upload their field observations, opening a new way to maintain the map database. The so established crowd-sourced data collection intensely influences the map quality; therefore, this method generates quality-related issues that are also to be analyzed.The second part of the paper is a case study, where a pilot site close to the university campus was selected. In this area, thousands of images were captured and uploaded into the Mapillary database. Artificial intelligence processes were applied for segmenting, classifying, and evaluating the content of the georeferenced imagery. The map database stores various object categories in the area, for example, pedestrian crossings, traffic signs, or trash cans. All extracted objects are available in georeferenced format, enabling spatial analyses to derive numeric quality indicators. The paper presents the complete results of this study.
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