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Autonomous driving went through numerous significant improvements over the past couple of years, including driver assistants that are already capable of executing an increasing number of complex tasks without the need for any human intervention. As a result of these changes, manufacturers are relying more and more on fast, cheap, and often better-quality simulations over real-world tests. To create these environments, the real world needs to be transformed to a digital, high-definition model. HD maps – for example, the XML-based, hierarchic OpenDRIVE format – aim to serve this purpose.The most important element of any realistic map format is the ability to check connectivity on the map in a convenient way, hence the need for topology. In HD maps, the description of junctions poses a significant challenge to the designers of the format, since they are essential yet complex topological elements. The representation of these junctions is still in progress, however, according to our analysis, the use of the current tools in OpenDRIVE can result in anomalies in the map.In the most recent release of OpenDRIVE (version 1.7), road-road and lane-lane connections are described using links consisting of a predecessor and a successor. These however, has to be described multiple times when the junction tag is used, resulting in duplicates in the model which can be easily exploited. Our proposed solution for this issue is the elimination of the junction tag, which not only gets rid of the anomalies without any loss of information, but it also significantly reduces the size of the model. In this paper, a detailed explanation is provided of this issue and the proposed solution with examples using OpenDRIVE models.

The development of self-driving vehicles has been efficiently accelerated by applying computer simulations. These environments can achieve the best result only if reality has been digitized: highly accurate and detailed maps are required. The high-definition (HD) maps aim to fulfill these requests. Companies and academia are seeking adequate technology, where OpenDRIVE standard seems to win this race.A software was developed to create high-definition road models for self-driving car testing purposes, which can also be used in driving simulators, for example, for testing vehicle prototypes.The data of a sample area was received located in northern Budapest, which included inconsistent information about the lanes of the road network. The top-priority task was to build a highly structured, consistent model satisfying this open standard, an increasingly popular modeling format to describe the environment for various simulating software. In order to do that, the relevant information had to be extracted from an AutoCAD file. The result was a collection of points that was added to a software module written in C++. The code has built a structure from those points, including determining connections between lanes, building roads from the lanes, determining connections between roads, adding road marks, and building the final OpenDRIVE model.Two different sets of data have been used thus far in the software: a circa 8 km long road section and a separate junction – meaning about 20,000 points to process. Two independent models were created; both were tested in various automotive simulators – including IPG CarMaker, probably the most well-known simulating tool used in car manufacturing – which showed that both are indeed correct. The overall time required to build those models from simple text files to well-structured OpenDRIVE models is no more than two minutes, while before the software development – when all of those aforementioned processes were done by humans – it required several weeks, if not months.The software is already capable of building basic road models but is nowhere near finished. Numerous other features can be added to it as well, for instance, traffic signs and signals and environmental elements such as roadside objects, trees, and buildings.

High-resolution computed micro-tomography is an important area of science, which correlates well with several experimental methodologies and serves as a basis for advanced computational physics studies, in which high-resolution images are used as input to different scientific simulation models. The dataset presented herein includes (raw) grayscale images obtained using the Bruker Skyscan 1272 X-Ray tomograph; filtered images acquired through contrast enhancement and noise reduction filters; and segmented images obtained by using the IsoData segmentation method. All images have a resolution of 2.25 µm (isometric voxels) and size of 1000 3  voxels., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s). Published by Elsevier Inc.)

Permeability is the key parameter for quantifying fluid flow in porous rocks. Knowledge of the spatial distribution of the connected pore space allows, in principle, to predict the permeability of a rock sample. However, limitations in feature resolution and approximations at microscopic scales have so far precluded systematic upscaling of permeability predictions. Here, we report fluid flow simulations in pore-scale network representations designed to overcome such limitations. We present a novel capillary network representation with an enhanced level of spatial detail at microscale. We find that the network-based flow simulations predict experimental permeabilities measured at lab scale in the same rock sample without the need for calibration or correction. By applying the method to a broader class of representative geological samples, with permeability values covering two orders of magnitude, we obtain scaling relationships that reveal how mesoscale permeability emerges from microscopic capillary diameter and fluid velocity distributions.

Lignin is a heterogeneous aromatic polymer responsible for cell wall stiffness and protection from pathogen attack. However, lignin represents a bottleneck to biomass degradation due to its recalcitrance related to the natural cell wall resistance to release sugars for fermentation or further processing. A biological approach involving genetics and molecular biology was used to disrupt lignin pathway synthesis and decrease lignin deposition. Here, we imaged three-dimensional fragments of the petioles of wild type and C4H lignin mutant Arabidopsis thaliana plants by synchrotron cryo-ptychography. The three-dimensional images revealed the heterogeneity of vessels, parenchyma, and fibre cell wall morphologies, highlighting the relation between disturbed lignin deposition and vessel implosion (cell collapsing and obstruction of water flow). We introduce a new parameter to accurately define cell implosion conditions in plants, and we demonstrate how cryo-ptychographic X-ray computed tomography (cryo-PXCT) provides new insights for plant imaging in three dimensions to understand physiological processes.

The NMR measurements of longitudinal and transverse relaxation times and its multidimensional correlations provide useful information about molecular dynamics. However, these experiments are very time-consuming, and many researchers proposed faster experiments to reduce this issue. This paper presents a new way to simultaneously perform T 2 -T 2 Exchange and T 1 -T 2 correlation experiments by taking the advantage of the storage time and the two steps phase cycling used for running the relaxation exchange experiment. The data corresponding to each step is either summed or subtracted to produce the T 2 -T 2 and T 1 -T 2 data, enhancing the information obtained while maintaining the experiment duration. Comparing the results from this technique with traditional NMR experiments it was possible to validate the method., (Copyright © 2018 Elsevier Inc. All rights reserved.)