We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage individually. The importance of such technology is that it can facilitate a spectrum of new derivative services, including cloud-assisted and cloud-controlled ADAS functions, dynamic map generation with analytics for traffic control and road infrastructure monitoring, a digital framework for operating vehicle testing grounds, logistics facilities, etc. In this paper, we constrain our discussion on the viability of the core concept and implement a system that provides a single service: the live visualization of our digital twin in a 3D simulation, which instantly and reliably matches the state of the real-world environment and showcases the advantages of real-time fusion of sensory data from various traffic participants. We envision this prototype system as part of a larger network of local information processing and integration nodes, i.e., the logically centralized digital twin is maintained in a physically distributed edge cloud.

High dynamic range (HDR) of illumination may cause serious distortion and information loss in the viewing and further processing of digital images. On the other hand, digital processing can often improve the visual quality of real-world photographs or views. Recently, HDR imaging techniques have become the focus of much research because of their high theoretical and practical importance. If the scene to be viewed or processed has HDR (dark and bright) illumination, these methods are able to help to detect details that a bright environment washes out, and they miss fewer details in a dark environment. By applying HDR techniques, the performance of different image processing and computer vision algorithms, information enhancement, and object and pattern recognition can also be improved. In this paper, a new tone reproduction algorithm is introduced, which may help to develop hard-to-view or nonviewable features and content of color images. The method applies a multiple exposure time image synthesization technique, where the red, green, and blue (RGB) color components of the pixels are separately handled. In the output, the corresponding (modified) color components are blended. As a result, a high-quality color HDR image is obtained, which contains the maximum level of details and color information.

A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.

The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a novel tensor-product based approach for representation of neural networks (NNs) is proposed. The NNs in this case stand for local models based on which a more complex parameter varying model can numerically be reconstructed and reduced using the higher order singular value decomposition (HOSVD). The HOSVD as well as the tensor-product based representation of NNs will be discussed in detail.

The high dynamic range of illumination may cause serious distortions and problems in the viewing and further processing of digital images. Important information can be hidden in the highly or extremely lowly illuminated parts. This paper deals with the reproduction of such images and introduces two new tone reproduction preprocessing algorithms which may help in developing the hardly viewable or nonviewable features and content of the images.