Road Points Research Articles

Research on Foreign Art and Culture Communication and Exchange of Chinese Tang Dynasty Music and Dance as the Carrier

The historic moment when Belt and Road is about to go through the first glorious decade, dance is an important cultural carrier and link of China s foreign art exchange. As the starting point of the ancient Silk Road, Chang an of the Tang Dynasty conceived music and dance in the Tang Dynasty as the peak of the development of ancient dance art. In the next key into the Belt and Road under the historical background of ten years, through the tang dynasty music dance as the carrier of the world of Chinese traditional culture and art communication, constantly use theory with practice, with practice sublimation theory, through the dance theory innovation, expression, innovation, communication form innovation, propaganda mode innovation, talent training innovation to concise sublimation, for the Belt and Road great strategy of extension and expand contribution art strength, realize the high quality development of the new era of tang dance.

Central Asia as a Continental Bridge or Thucydides Trap?

When the Western press and media report on Xinjiang, they tend to focus on the situation of the Uyghurs in this autonomous region of China. The fact that Xinjiang is of the highest strategic importance for China as a raw materials base, gateway to Central Asia and starting point of the New Silk Road, which is orientated towards Europe, and that real geopolitics is all about precisely these things, is often neglected. Of course, the starting point of the New Silk Road is in the strategists' crosshairs and it goes without saying that Central Asia and the South Caucasus are also targeted. After all, the shortest transport corridors to Europe run through these areas, now that the transport routes via Russia have been cancelled by Russia's war in Ukraine. This is where the Chinese New Silk Road and the European Global Gateway meet. What is the situation in Central Asia? What role does the International Trans-Caspian Transport Route play? How are China, the USA and Europe acting where the future of the New Silk Road is ultimately at stake? This article aims to provide a few answers to these questions, which should therefore only be understood as an approach to the topic of "Central Asia as a continental bridge or Thucydides' trap?".

Clinical Features, Genetic Landscape and Management of Behçet's Syndrome: A Comprehensive Review

Behçet's syndrome is a systemic inflammatory disorder of unknown origin, presenting with diverse symptoms such as recurrent oral and genital ulcers, skin lesions, and uveitis, and can impact multiple organ systems. Diagnosis relies primarily on clinical evaluation due to the lack of specific diagnostic tests. Management requires a multidisciplinary approach to control inflammation and alleviate symptoms, utilizing treatments like corticosteroids, immunosuppressive agents, and biological therapies. The higher prevalence of Behçet's syndrome along the Silk Road points to significant environmental and genetic influences. Despite progress in understanding its clinical manifestations and treatment options, the underlying mechanisms of the disease remain unclear. Future research is crucial to uncover the disease's pathophysiology and refine treatment strategies, aiming to prevent severe complications such as blindness and neurological impairment. This comprehensive review explores the clinical features, genetic background, and management strategies for Behçet's syndrome, highlighting the potential of precision medicine to improve patient care.

LESA-Net: Semantic segmentation of multi-type road point clouds in complex agroforestry environment

Point-cloud semantic segmentation is a visual task essential for agricultural robots to comprehend natural agroforestry environments. However, owing to the extremely large amount of point-cloud data in agroforestry environments, learning effective features for semantic segmentation from large-scale point clouds is challenging. Therefore, to address this issue and achieve accurate semantic segmentation of different types of road-surface point clouds in large-scale agroforestry environments, this study proposes a point-cloud semantic segmentation network framework based on double-distance self-attention. First, a point-cloud local feature enhancement module is proposed. This module primarily extends the receptive field and enhances the generalizability of multidimensional features by incorporating reflection intensity information and a spatial feature-encoding block that is enhanced with contextual semantic information. Second, we introduce a dual-distance attention pooling (DDAPS) block based on the self-attention mechanism. This block initially learns the feature representation of the local neighborhood of each point through the self-attention mechanism. Then, it uses the DDAPS block to aggregate more discriminative local neighborhood point features. Finally, extensive experimental results on large-scale point-cloud datasets, SemanticKITTI and RELLIS-3D, demonstrate that our algorithm outperforms similar algorithms in large-scale agroforestry environments.

ANALYSIS OF RESOLVING EFFICIENT DOMINATING SET AND ITS APPLICATION SCHEME IN SOLVING ETLE PROBLEMS

This research focuses on the analysis of Resolving Efficient Dominating Set (REDS) and its application in solving Electronic Traffic Law Enforcement (ETLE) problems using the Spatial Temporal Graph Neural Network (STGNN). Resolving Efficient Dominating Set (REDS) is a concept in graph theory that studies a set of points in a graph that efficiently monitors other points. It involves ensuring that each point v ∈ V (G) - D is dominated by exactly one point in D, with no adjacent points in D, and the representation of point v ∈ V (G) concerning D is not the same, which is termed as a resolving efficient dominating set. In the context of Electronic Traffic Law Enforcement (ETLE), the analysis of REDS has a significant impact. The theorem resulting from the analysis of REDS enables the determination of the number of traffic violation sensors required. Furthermore, by taking simulation data from road points, violation forecasting can be performed. The accurate predictions from this forecasting can assist authorities in anticipating and addressing traffic violation issues more effectively.

Pemodelan Identifikasi Objek Kendaraan Bermotor Menggunakan Faster Region based Convolutional Neural Network (R-CNN) Berbasis Python

The vehicles are currently experiencing a surge in number and variation. This is evident from the kinds of vehicles that are passing through the highway area. The rise in the number of motorized vehicles will surely give a squeeze to the traffic density. The increase in the number of motor vehicles is one of the biggest factors in the impact of the congestion. The congestion can also cause damage to the highway. It's supposed to be the focus of the local government in dealing with the problem. Each road point has its own potential, so it is necessary to have a calculation in identifying the number of vehicles and the type of vehicles that are slipped on the road. Motor vehicle identification can be solved using the Faster Region based Convolutional Neural Network approach. Faster R-CNN is a deep learning architecture used to detect inside computers. Research will run at several highway points to take samples of video at a certain time, for identified the type of vehicle. Vehicle labelling will facilitate the calculation of the number of vehicles crossing the road in a given unit of time. The vehicle identification needs are used to see the density of the highway so that it can help the local government in making the right decision or solution to reduce the traffic density. The results of research such as quantitative data can be easily used to give the right picture and decision.

O2SAT: Object-Oriented-Segmentation-Guided Spatial-Attention Network for 3D Object Detection in Autonomous Vehicles

Autonomous vehicles (AVs) strive to adapt to the specific characteristics of sustainable urban environments. Accurate 3D object detection with LiDAR is paramount for autonomous driving. However, existing research predominantly relies on the 3D object-based assumption, which overlooks the complexity of real-world road environments. Consequently, current methods experience performance degradation when targeting only local features and overlooking the intersection of objects and road features, especially in uneven road conditions. This study proposes a 3D Object-Oriented-Segmentation Spatial-Attention (O2SAT) approach to distinguish object points from road points and enhance the keypoint feature learning by a channel-wise spatial attention mechanism. O2SAT consists of three modules: Object-Oriented Segmentation (OOS), Spatial-Attention Feature Reweighting (SFR), and Road-Aware 3D Detection Head (R3D). OOS distinguishes object and road points and performs object-aware downsampling to augment data by learning to identify the hidden connection between landscape and object; SFR performs weight augmentation to learn crucial neighboring relationships and dynamically adjust feature weights through spatial attention mechanisms, which enhances the long-range interactions and contextual feature discrimination for noise suppression, improving overall detection performance; and R3D utilizes refined object segmentation and optimized feature representations. Our system forecasts prediction confidence into existing point-backbones. Our method’s effectiveness and robustness across diverse datasets (KITTI) has been demonstrated through vast experiments. The proposed modules seamlessly integrate into existing point-based frameworks, following a plug-and-play approach.

RailPC: A large‐scale railway point cloud semantic segmentation dataset

AbstractSemantic segmentation in the context of 3D point clouds for the railway environment holds a significant economic value, but its development is severely hindered by the lack of suitable and specific datasets. Additionally, the models trained on existing urban road point cloud datasets demonstrate poor generalisation on railway data due to a large domain gap caused by non‐overlapping special/rare categories, for example, rail track, track bed etc. To harness the potential of supervised learning methods in the domain of 3D railway semantic segmentation, we introduce RailPC, a new point cloud benchmark. RailPC provides a large‐scale dataset with rich annotations for semantic segmentation in the railway environment. Notably, RailPC contains twice the number of annotated points compared to the largest available mobile laser scanning (MLS) point cloud dataset and is the first railway‐specific 3D dataset for semantic segmentation. It covers a total of nearly 25 km railway in two different scenes (urban and mountain), with 3 billion points that are finely labelled as 16 most typical classes with respect to railway, and the data acquisition process is completed in China by MLS systems. Through extensive experimentation, we evaluate the performance of advanced scene understanding methods on the annotated dataset and present a synthetic analysis of semantic segmentation results. Based on our findings, we establish some critical challenges towards railway‐scale point cloud semantic segmentation. The dataset is available at https://github.com/NNU‐GISA/GISA‐RailPC, and we will continuously update it based on community feedback.

Convolutional Neural Network and LSTM for Seat Belt Detection in Vehicles using YOLO3

The application of an electronic violation detection system has begun to be implemented in many countries using CCTV cameras installed at highway and toll road points. However, the development of a violation detection system using data in the form of images that have a high level of accuracy is still a challenge for researchers. Several types of violations detected include the use of seat belts, the use of cell phones while driving, which is influenced by the number of vehicles, vehicle speed and lighting, which can increase the difficulty in the detection process. This research developed a traffic violation detection system using a hybrid model, namely the CNN and LSTM algorithms for the application of discipline using seat belts. The dataset was obtained from RoboFlow Universe with a total of 199 front view car images consists of 82 using seatbelts and 78 not using seatbelts for the training process. The CNN algorithm plays a role in the feature extraction process from input image data, while the LSTM algorithm plays a role in the prediction process. Additionally, the performance evaluation of the CNN+LSTM algorithm will be measured using the accuracy value to measure the performance of the training process and testing process. When measuring the performance of the training process, it will be compared with several basic detection models used, such as CNN, VGG16, ResNet50, MobileNetV2, Yolo3, Yolo3+LSTM. The test results show that Yolo3+LSTM has a higher accuracy compared to the others, at 89%. Next, in the testing process, the CNN+LSTM model will be compared with the basic method, namely CNN. The test results show that the CNN+LSTM models have a higher accuracy of 89%. Meanwhile, in the basic CNN model, the resulting accuracy was 85%.

The Effectiveness of Patrol Implementation by the Padang Police Traffic Unit in Combating Wild Racing

One of the efforts made to prevent Kamtibmas disturbances by the Police is to conduct patrols, which are regulated in the Regulation of the Head of the Security Maintenance Agency of the National Police of the Republic of Indonesia Number 1 of 2017 concerning Patrols. This is important because it is an essential need for everyone in various aspects of life, and is free from all worries, threats, disturbances of kamtibmas. One of the triggers for kamtibmas disturbances is the wild racing action that occurred in Padang City. Research is descriptive analytical with a normative juridical approach supported by an empirical juridical approach. Based on the results of research and analysis obtained first, the effectiveness of the implementation of patrols by the Padang Police Traffic Unit in tackling wild racing has not been maximally proven until now there are still wild racing actions at several road points in Padang City. In addition, the actions of the Police only use repressive measures, namely fines based on traffic violations. Second, the obstacles encountered in the implementation of patrols by the Padang Police Traffic Unit in tackling wild racing include internal obstacles , namely the absence of a Special Unit that handles wild racing actions so that it is limited to control and disbandment, as well as the lack of personnel of the Padang Police Station considering the large number of illegal racing actors who often move around in holding wild racing actions. External constraints are the absence of integrated cameras with traffic units at road points that are used as wild racing actions so that patrols must still be carried out directly to areas prone to wild racing and betting culture among people who love speed racing and the absence of special facilities and places or racing arenas in Padang City.

Predicting black ice-related accidents with probabilistic modeling using GIS-based Monte Carlo simulation.

Black ice, a phenomenon that occurs abruptly owing to freezing rain, is difficult for drivers to identify because it mirrors the color of the road. Effectively managing the occurrence of unforeseen accidents caused by black ice requires predicting their probability using spatial, weather, and traffic factors and formulating appropriate countermeasures. Among these factors, weather and traffic exhibit the highest levels of uncertainty. To address these uncertainties, a study was conducted using a Monte Carlo simulation based on random values to predict the probability of black ice accidents at individual road points and analyze their trigger factors. We numerically modeled black ice accidents and visualized the simulation results in a geographical information system (GIS) by employing a sensitivity analysis, another feature of Monte Carlo simulations, to analyze the factors that trigger black ice accidents. The Monte Carlo simulation allowed us to map black ice accident occurrences at each road point on the GIS. The average black ice accident probability was found to be 0.0058, with a standard deviation of 0.001. Sensitivity analysis using Monte Carlo simulations identified wind speed, air temperature, and angle as significant triggers of black ice accidents, with sensitivities of 0.354, 0.270, and 0.203, respectively. We predicted the probability of black ice accidents per road section and analyzed the primary triggers of black ice accidents. The scientific contribution of this study lies in the development of a method beyond simple road temperature predictions for evaluating the risk of black ice occurrences and subsequent accidents. By employing Monte Carlo simulations, the probability of black ice accidents can be predicted more accurately through decoupling meteorological and traffic factors over time. The results can serve as a reference for government agencies, including road traffic authorities, to identify accident-prone spots and devise strategies focused on the primary triggers of black ice accidents.

Two is better than one: digital siblings to improve autonomous driving testing

Simulation-based testing represents an important step to ensure the reliability of autonomous driving software. In practice, when companies rely on third-party general-purpose simulators, either for in-house or outsourced testing, the generalizability of testing results to real autonomous vehicles is at stake. In this paper, we enhance simulation-based testing by introducing the notion of digital siblings—a multi-simulator approach that tests a given autonomous vehicle on multiple general-purpose simulators built with different technologies, that operate collectively as an ensemble in the testing process. We exemplify our approach on a case study focused on testing the lane-keeping component of an autonomous vehicle. We use two open-source simulators as digital siblings, and we empirically compare such a multi-simulator approach against a digital twin of a physical scaled autonomous vehicle on a large set of test cases. Our approach requires generating and running test cases for each individual simulator, in the form of sequences of road points. Then, test cases are migrated between simulators, using feature maps to characterize the exercised driving conditions. Finally, the joint predicted failure probability is computed, and a failure is reported only in cases of agreement among the siblings. Our empirical evaluation shows that the ensemble failure predictor by the digital siblings is superior to each individual simulator at predicting the failures of the digital twin. We discuss the findings of our case study and detail how our approach can help researchers interested in automated testing of autonomous driving software.

Residential exposure to traffic pollution and mammographic density in premenopausal women

BackgroundMammographic density (MD) is the most important breast cancer biomarker. Ambient pollution is a carcinogen, and its relationship with MD is unclear. This study aims to explore the association between exposure to traffic pollution and MD in premenopausal women. MethodologyThis Spanish cross-sectional study involved 769 women attending gynecological examinations in Madrid. Annual Average Daily Traffic (AADT), extracted from 1944 measurement road points provided by the City Council of Madrid, was weighted by distances (d) between road points and women's addresses to develop a Weighted Traffic Exposure Index (WTEI). Three methods were employed: method-1 (1dAADT), method-2 (1dAADT), and method-3 (e1dAADT). Multiple linear regression models, considering both log-transformed percentage of MD and untransformed MD, were used to estimate MD differences by WTEI quartiles, through two strategies: “exposed (exposure buffers between 50 and 200 m) vs. not exposed (>200 m)”; and “degree of traffic exposure”. ResultsResults showed no association between MD and traffic pollution according to buffers of exposure to the WTEI (first strategy) for the three methods. The highest reductions in MD, although not statistically significant, were detected in the quartile with the highest traffic exposure. For instance, method-3 revealed a suggestive inverse trend (eβQ1 = 1.23, eβQ2 = 0.96, eβQ3 = 0.85, eβQ4 = 0.85, p-trend = 0.099) in the case of 75 m buffer. Similar non-statistically significant trends were observed with Methods-1 and -2. When we examined the effect of traffic exposure considering all the 1944 measurement road points in every participant (second strategy), results showed no association for any of the three methods. A slightly decreased MD, although not significant, was observed only in the quartile with the highest traffic exposure: eβQ4 = 0.98 (method-1), and eβQ4 = 0.95 (methods-2 and -3). ConclusionsOur results showed no association between exposure to traffic pollution and MD in premenopausal women. Further research is needed to validate these findings.

Examining the possibility of short-term prediction of traffic volume in smart city control systems with the use of regression models

This article deals with issues related to the optimization of traffic management in modern cities, the so-called Smart City. In particular, the article presents the process of evolution of the traffic flow prediction model at a selected crossroads in a selected city in Poland - the city of Rzeszów. Rzeszow is an example of a smart city equipped with an extensive system of real-time data collection and processing from multiple road points in the city. The research was aimed at a detailed analysis of the feasibility and degree of fit of different variants of the regression model: linear, polynomial, trigonometric, polynomial-trigonometric, and regression-based Random Forest algorithm. Several studies were carried out evaluating different generations of models, in particular, an analysis was carried out based on which the superiority of the trigonometric model was demonstrated. This model had the best fit and the lowest error rate, which could be a good conclusion for widespread use and implementation in Smart City supervisory systems.

Classification of Typical Static Objects in Road Scenes Based on LO-Net

Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the “Unite_module” for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%.

Examining the English Publicity Materials of the Cultural Heritage in Xi’an from the Perspective of Functional Equivalence Theory

Xi’an, one of the political, economic and cultural centers of ancient China and the starting point of the New Silk Road, has increasingly become an important window through which Chinese culture is introduced to the outside world. The publicity materials of Xi’an cultural attractions contain rich Chinese cultural elements. At present, the study of their English translation in the academic circle is still fragmented, relatively macro and insufficient in the cases, which seriously affects the communication and exchange between Shaanxi and foreign cultures. In this essay, a series of important cultural terms were selected from the publicity materials of Xi’an cultural attractions issued by Shaanxi Tourism Bureau. Then the literal and cultural meanings of these terms in Chinese and English were compared so that the problems in their translation were studied. It is found that there were three kinds of translation problems: disparity in both the literal and cultural meaning; disparity in acquired meaning; disparity in associated cultural meaning. The results of this study have some theoretical value in the translation of important cultural terms, and have certain practical value in introducing the Chinese culture to the rest of the world and improve the image of Xi’an.

Geotourism And Cultural Prospects of Rupat Island, Bengkalis, Riau

The area of interest is located on Rupat Island, in the north of the Central Sumatra Basin, and faces directly the Malacca Strait, specifically in the North Rupat District, Bengkalis Regency, Riau Province, with the development of natural tourism objects and attractions. The geology of this area consists of two main formations, namely the Old Superficial Sedimentation Formation (QP) consisting of silt, clay, gravel, and plant residues, and the formation of young sedimentation (QH) consists of clay, silt, slippery gravel, residual plants, and peat swamps. There is also silica sand that is only found on the coastline in the north of Rupat Island, which is greatly influenced by the current of the Malacca Strait as a sediment transportation agent from Indian Ocean and South China Sea. There is a fault structure along with the silica sand deposition creating the main objects of geotourism that are found in the northern Rupat area in the form of Lapin Beach, Pesona Beach, Tanjung Api Beach, and Beting Aceh Beach. To visit the geotourism area both road and sea by boat, also made supporting facilities such as homestays, local road points, and signboard. There is also a diversity of flora and fauna in the form of pine trees, and mangrove forests, mammals such as deer, birds such as hombills, crows, and primates such as long-tailed monkeys, beruk and lutung are quite easy to find along the mangrove forest. There is a diversity of Malay cultures such as Zapin Dance and Safar Bathing. This research was aiming to develop geotourism prospects that have not been explored and create new potential for tourism products in North Rupat, Rupat Island.

GAN-BiLSTM network for field-road classification on imbalanced GNSS recordings

Field-road classification is a key task in processing spatio-temporal trajectories in the agricultural field. This task involves classifying agricultural machinery trajectories into field operation and road driving trajectories. Accurately classifying these trajectories is essential for precision agriculture. However, the existing methods for field-road classification do not address the issue of data imbalance between the numbers of field and road points in trajectories. To improve the accuracy of field-road classification and solve the data imbalance problem in agricultural machinery trajectories, a generative adversarial network-bidirectional long short-term memory network (GAN-BiLSTM) field-road classification model is proposed in this paper. First, we use a GAN for data augmentation to obtain a balanced dataset. Second, we propose a BiLSTM with attention mechanism (Att-BiLSTM) to capture data features. Finally, we train the model using focal loss to further learn points that are difficult to classify for field-road classification. To evaluate the effectiveness of our proposed model, we conducted experiments and compared our results with those of the current field-road classification methods on 160 agricultural trajectory samples. Our model achieved 92.3% accuracy and a 92.1% F1 score, exhibiting improvements of 5.9% in accuracy and 6.4% in F1 score compared with the current state-of-the-art method. The source code is publicly available at https://github.com/hysyyds/GAN-BiLSTM.

Automated Road Extraction and Analysis from LiDAR Point Cloud Data Using Local Optimization

Applying point cloud data to road analysis is crucial for obtaining practical features for segmenting and classifying road point clouds. This study proposes a multi-step method for extracting road points and road network structures from urban Light Detection and Ranging (LiDAR) point cloud data. The first step is a two-step algorithm of coarse grid classification and local optimization of point cloud fine classification. This step extracts road point clouds from various parts of the city. The second step involves the road-point cloud splicing work. Finally, we extract the urban road network structure according to the point cloud of the urban main road and calculate the width of each road. We evaluate the method's feasibility using four urban road point clouds. Experimental results show that the proposed method can quickly and accurately extract road points, obtaining a road data accuracy and integrity of >94% and a road width estimated relative error of <7%.

Analysis of Technical Key Points in Road and Bridge Construction Management

With the rapid development of the country, the overall strength of the country is also increasing. With the rapid development of various industries in the country, more new technologies have been introduced and more new materials have been adopted in highway and bridge construction. At the same time, improving the quality of road and bridge construction and prolonging the service life of road and bridge engineering are also the primary tasks of construction enterprises. Therefore, in order to improve the quality of road and bridge engineering and extend its service life, it is necessary to improve the quality of road and bridge engineering on the premise of ensuring the construction focus. In the process of construction, the construction unit must effectively and reasonably choose the bridge construction technology, technological process and construction points that conform to itself, and only in this way can the quality of bridge engineering be guaranteed. In the process of road and bridge construction, the construction unit must comprehensively consider all aspects, carefully and conscientiously do every procedure, and take effective measures against various problems to ensure the construction quality, save costs and promote the development of road and bridge undertakings in China.