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Journal articles on the topic 'Road markings'
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1
Babić, Dario, Anđelko Ščukanec, Darko Babić, and Mario Fiolić. "Model for Predicting Road Markings Service Life." Baltic Journal of Road and Bridge Engineering 14, no. 3 (September 26, 2019): 341–59. http://dx.doi.org/10.7250/bjrbe.2019-14.447.
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Road markings provide drivers with the information necessary for their comfortable and safe journey. To achieve that, they must be visible in all driving conditions, which means that they have to be renewed and maintained in a timely manner. This research aims to develop a model for predicting the service life of paint, thermoplastic and agglomerate cold plastic road markings. The research comprised 5218.00 km (115 roads) of solvent-borne paint road markings, 579.00 km (30 roads) of thermoplastic and 4979.00 km (30 roads) of agglomerate cold plastic road markings. The retroreflectivity of road markings was measured two times (after renewal and after winter) using the dynamic measuring method. Service life prediction model was developed for each material based on factors such as initial retroreflectivity, road marking age and position, annual average daily traffic and winter maintenance activity of the road. Developed models were tested using a new data set, and all three models showed satisfactory accuracy. The application of the model enables road authorities to optimise road-marking maintenance activities, while at the same time, ensuring the level of visibility required by drivers.
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Storsæter, Ane Dalsnes, Kelly Pitera, and Edward McCormack. "Camera-Based Lane Detection—Can Yellow Road Markings Facilitate Automated Driving in Snow?" Vehicles 3, no. 4 (October 13, 2021): 661–90. http://dx.doi.org/10.3390/vehicles3040040.
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Road markings are beneficial to human drivers, advanced driver assistance systems (ADAS), and automated driving systems (ADS); on the contrary, snow coverage on roads poses a challenge to all three of these groups with respect to lane detection, as white road markings are difficult to distinguish from snow. Indeed, yellow road markings provide a visual contrast to snow that can increase a human drivers’ visibility. Yet, in spite of this fact, yellow road markings are becoming increasingly rare in Europe due to the high costs of painting and maintaining two road marking colors. More importantly, in conjunction with our increased reliance on automated driving, the question of whether yellow road markings are of value to automatic lane detection functions arises. To answer this question, images from snowy conditions are assessed to see how different representations of colors in images (color spaces) affect the visibility levels of white and yellow road markings. The results presented in this paper suggest that yellow markings provide a certain number of benefits for automated driving, offering recommendations as to what the most appropriate color spaces are for detecting lanes in snowy conditions. To obtain the safest and most cost-efficient roads in the future, both human and automated drivers’ actions must be considered. Road authorities and car manufacturers also have a shared interest in discovering how road infrastructure design, including road marking, can be adapted to support automated driving.
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Storsæter, Ane Dalsnes, Kelly Pitera, and Edward McCormack. "Camera-Based Lane Detection—Can Yellow Road Markings Facilitate Automated Driving in Snow?" Vehicles 3, no. 4 (October 13, 2021): 661–90. http://dx.doi.org/10.3390/vehicles3040040.
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Road markings are beneficial to human drivers, advanced driver assistance systems (ADAS), and automated driving systems (ADS); on the contrary, snow coverage on roads poses a challenge to all three of these groups with respect to lane detection, as white road markings are difficult to distinguish from snow. Indeed, yellow road markings provide a visual contrast to snow that can increase a human drivers’ visibility. Yet, in spite of this fact, yellow road markings are becoming increasingly rare in Europe due to the high costs of painting and maintaining two road marking colors. More importantly, in conjunction with our increased reliance on automated driving, the question of whether yellow road markings are of value to automatic lane detection functions arises. To answer this question, images from snowy conditions are assessed to see how different representations of colors in images (color spaces) affect the visibility levels of white and yellow road markings. The results presented in this paper suggest that yellow markings provide a certain number of benefits for automated driving, offering recommendations as to what the most appropriate color spaces are for detecting lanes in snowy conditions. To obtain the safest and most cost-efficient roads in the future, both human and automated drivers’ actions must be considered. Road authorities and car manufacturers also have a shared interest in discovering how road infrastructure design, including road marking, can be adapted to support automated driving.
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Setyawan P., Arkham, Tamrin Mallawangeng, and Nurhadijah Yunianti. "Evaluasi Rambu Lalu Lintas Dan Marka Jalan Ruas Veteran Utara Dan Veteran Selatan." Jurnal Penelitian Teknik Sipil Konsolidasi 1, no. 3 (September 30, 2023): 226–31. http://dx.doi.org/10.56326/jptsk.v1i3.1605.
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The empirical phenomenon of transportation problems, mainly on the Veteran Utara – Veteran Selatan section, is traffic and lack of driver discipline, causing traffic circulation disturbances, especially on the Veteran Utara – Veteran Selatan road section, which is irregular on a regular basis for road users to the presence of signs and roads. The aim of the research is to determine the placement and need for signs and road markings in North Veteran and South Veteran and to determine the influence of the placement of road signs and markings on North Veteran and South Veteran roads.As for the results of the study, the most severe existing locations are roads that have reverse signposts but do not have facilities for turning back so as to increase road users and increase the potential for accidents, while for the placement of markings there are still many locations that require re-marking because they are starting to fade markings along STA 0+000 – STA 0+3000 are like longitudinal dotted lines and markings crossing the stop line (zebra cross), with the fading of these markings will reduce the level of discipline of road users and increase the potential for traffic accidents
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Kang, Seokchan, Jeongwon Lee, and Jiyeong Lee. "Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from a Mobile Mapping System Point Cloud Using Oriented Bounding Box Collision-Detection Techniques." Remote Sensing 15, no. 19 (September 22, 2023): 4656. http://dx.doi.org/10.3390/rs15194656.
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Advancements in data-acquisition technology have led to the increasing demand for high-precision road data for autonomous driving. Specifically, road boundaries and linear road markings, like edge and lane markings, provide fundamental guidance for various applications. Unfortunately, their extraction usually requires labor-intensive manual work, and the automatic extraction, which can be applied universally for diverse curved road types, presents a challenge. Given this context, this study proposes a method to automatically extract road boundaries and linear road markings by applying an oriented bounding box (OBB) collision-detection algorithm. The OBBs are generated from a reference line using the point cloud data’s position and intensity values. By applying the OBB collision-detection algorithm, road boundaries and linear road markings can be extracted efficiently and accurately in straight and curved roads by adjusting search length and width to detect OBB collision. This study assesses horizontal position accuracy using automatically extracted and manually digitized data to verify this method. The resulting RMSE for extracted road boundaries is +4.8 cm and +5.3 cm for linear road markings, indicating that high-accuracy road boundary and road marking extraction was possible. Therefore, our results demonstrate that the automatic extraction adjusting OBB detection parameters and integrating the OBB collision-detection algorithm enables efficient and precise extraction of road boundaries and linear road markings in various curving types of roads. Finally, this enhances its practicality and simplifies the implementation of the extraction process.
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Wu, Wenxiu, Xiaoyong Zou, Jing Liu, Yonglin Hu, Zhong Lv, Ruijian Zheng, and Zhen Liu. "Research on the Anti-Fouling Properties of Double-Coated Road Markings." E3S Web of Conferences 512 (2024): 03037. http://dx.doi.org/10.1051/e3sconf/202451203037.
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The article analyses the influencing factors of the anti-fouling properties of road traffic markings and researches a double-coated, anti-fouling road marking with good heat stability and high coating density. The anti-fouling performance of the road marking during its use period was experimentally analysed using SPSS software. The results indicate that the anti-fouling performance of the double-coated road marking is significantly better than that of thermoplastic markings and two-component markings, and it has a promising prospect for widespread application.
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Pan, Y., B. Yang, S. Li, H. Yang, Z. Dong, and X. Yang. "AUTOMATIC ROAD MARKINGS EXTRACTION, CLASSIFICATION AND VECTORIZATION FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1089–96. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1089-2019.
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<p><strong>Abstract.</strong> To meet the demands of various applications such as high definition navigation map production for unmanned vehicles and road reconstruction and expansion engineering, this paper proposes an effective and efficient approach to automatically extract, classify and vectorize road markings from Mobile Laser Scanning (MLS) point clouds. Firstly, the MLS point cloud is segmented to ground and non-ground points. Secondly, several geo-reference images are generated and further used to detect road markings pixels under an image processing scheme. Thirdly, road marking point clouds are retrieved from the image and further segmented into connected objects. Otsu thresholding and Statistic Outlier Remover are adopted to refine the road marking objects. Next, each road marking objects are classified into several categories such as boundary lines, rectangle road markings, etc. based on its bounding box information. Other irregular road markings are classified by a model matching scheme. Finally, all classified road markings are vectorized as closed or unclosed polylines after reconnecting the breaking boundary lines. Comprehensive experiments are done on various MLS point clouds of both the urban and highway scenarios, which show that the precision and recall of the proposed method is higher than 95% for road marking extraction and as high as 93% for road marking classification on highway scenarios. The ratio is 92% and 85% for urban scenarios.</p>
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Chou, Chia-Pei, Kin-Wai Leong, Ai-Chin Chen, and Yao-Xuan Lee. "Road marking retroreflectivity study via a visual algorithm." International Journal of Pavement Research and Technology 13, no. 6 (November 2020): 614–20. http://dx.doi.org/10.1007/s42947-020-6001-x.
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AbstractThe retroreflectivity (Rl) of road markings is important and should be inspected and maintained throughout their service life. The specifications are provided by European nations, the United States, and many other countries. Although acceptance tests ensure the good Rl quality of newly placed road markings, the RL values of all in-service road markings are rather difficult to inspect by using currently available devices. This study, therefore, aims to determine the relationship between Rl and corresponding image brightness of yellow road markings to evaluate their visibility by analyzing recorded images captured at night. An integrated algorithm was developed to analyze recorded images continuously for identifying road marking brightness 30 m away from a vehicle. Field experiments on three types of road marking materials were performed and repeated at four separate locations. The findings provide a promising direction for using the image brightness of road markings to predict their field Rl. However, limitations of this study are discussed and suggestions for future direction are presented.
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Ye, Chengming, Hongfu Li, Ruilong Wei, Lixuan Wang, Tianbo Sui, Wensen Bai, and Pirasteh Saied. "Double Adaptive Intensity-Threshold Method for Uneven Lidar Data to Extract Road Markings." Photogrammetric Engineering & Remote Sensing 87, no. 9 (September 1, 2021): 639–48. http://dx.doi.org/10.14358/pers.20-00099.
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Due to the large volume and high redundancy of point clouds, there are many dilemmas in road-marking extraction algorithms, especially from uneven lidar point clouds. To extract road markings efficiently, this study presents a novel method for handling the uneven density distribution of point clouds and the high reflection intensity of road markings. The method first segments the point-cloud data into blocks perpendicular to the vehicle trajectory. Then it applies the double adaptive intensity-threshold method to extract road markings from road surfaces. Finally, it performs an adaptive spatial density filter based on the density distribution of point-cloud data to remove false road-marking points. The average completeness, correctness, and F measure of road-marking extraction are 0.827, 0.887, and 0.854, respectively, indicating that the proposed method is efficient and robust.
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KEMSLEY, JYLLIAN. "road markings." Chemical & Engineering News 88, no. 36 (September 6, 2010): 67. http://dx.doi.org/10.1021/cen-v088n036.p067.
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Chun, Chanjun, Taehee Lee, Sungil Kwon, and Seung-Ki Ryu. "Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation." Sensors 20, no. 19 (September 28, 2020): 5560. http://dx.doi.org/10.3390/s20195560.
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Road markings constitute one of the most important elements of the road. Moreover, they are managed according to specific standards, including a criterion for a luminous contrast, which can be referred to as retroreflection. Retroreflection can be used to measure the reflection properties of road markings or other road facilities. It is essential to manage retroreflection in order to improve road safety and sustainability. In this study, we propose a dynamic retroreflection estimation method for longitudinal road markings, which employs a luminance camera and convolutional neural networks (CNNs). The images that were captured by a luminance camera were input into a classification and regression CNN model in order to determine whether the longitudinal road marking was accurately acquired. A segmentation model was also developed and implemented in order to accurately present the longitudinal road marking and reference plate if a longitudinal road marking was determined to exist in the captured image. The retroreflection was dynamically measured as a driver drove along an actual road; consequently, the effectiveness of the proposed method was demonstrated.
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Li, Fu Jie, Xin Ye Cao, Shuai Heng Liu, and Lei Lei He. "Transition from Reflective to Energy-Storing Self-Illumination in Road Markings: A Review." Advanced Materials Research 1176 (April 28, 2023): 63–76. http://dx.doi.org/10.4028/p-v6918o.
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Road markings regulate and direct traffic by conveying specific information. It is of great significance to develop new road marking materials and improve the visibility of marking materials for improving traffic efficiency and ensuring traffic safety. This paper summarized the development status of various reflective road markings at home and abroad. In addition, the energy storage luminescent fluorescent/phosphor marking lines in road marking was emphatically generalized to evaluate the advantages and disadvantages of different improvement methods. Overall, strontium aluminate doped with Eu2+ co-doped with Dy3+ (SrAl2O4:Eu2+, Dy3+) phosphors and self-luminous pavement for energy storage had great prospects in improving road safety and reducing energy consumption.
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Burghardt, Tomasz E., Anton Pashkevich, and Jacek Bartusiak. "Solution for a two-year renewal cycle of structured road markings." Roads and Bridges - Drogi i Mosty 20, no. 1 (March 5, 2021): 5–18. http://dx.doi.org/10.7409/rabdim.021.001.
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Road markings are a necessary safety feature that guides drivers. For visibility in darkness, when accident risk and severity are the highest, road markings must be reflectorised with glass beads to be visible when vehicle headlights shine on them. Renewal of thick-layer structured road markings that have lost retroreflectivity but have retained the structure mostly intact is performed through application of thin-layer paint coatings. Based on a field experiment, it was demonstrated that selection of premium glass beads profoundly extends the durability of the renewed markings, which could be further enhanced through the use of high-performance waterborne paint. Whereas the standard system based on solventborne paint and typically used glass beads failed after a single winter, the use of premium road marking system at least doubled the service life. The extended road marking service life translates to 50% savings in material consumption, 50-97% lower emissions of volatile organic compounds, and a decrease in long-term financial expense by 5-26%.
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Wu, Junjie, Wen Liu, and Yoshihisa Maruyama. "Automated Road-Marking Segmentation via a Multiscale Attention-Based Dilated Convolutional Neural Network Using the Road Marking Dataset." Remote Sensing 14, no. 18 (September 9, 2022): 4508. http://dx.doi.org/10.3390/rs14184508.
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Road markings, including road lanes and symbolic road markings, can convey abundant guidance information to autonomous driving cars. However, recent works have paid less attention to the recognition of symbolic road markings compared with road lanes. In this study, a road-marking-segmentation dataset named the RMD (Road Marking Dataset) is introduced to compensate for the lack of datasets and the limitations of the existing datasets. Furthermore, we propose a novel multiscale attention-based dilated convolutional neural network (MSA-DCNN) to tackle the proposed RMD. The proposed method employs multiscale attention to merge the weighting outputs of adjacent multiscale inputs, and dilated convolution to capture spatial-context information. The performance analysis shows that the proposed MSA-DCNN yields the best results by combining multiscale attention and dilated convolution. Additionally, the proposed method gains the mIoU of 74.88%, which is a significant improvement over the existing techniques.
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Azmi, Nur Hanisah, Ali Sophian, and Ali Aryo Bawono. "Deep-learning-based detection of missing road lane markings using YOLOv5 algorithm." IOP Conference Series: Materials Science and Engineering 1244, no. 1 (June 1, 2022): 012021. http://dx.doi.org/10.1088/1757-899x/1244/1/012021.
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Abstract Road lane markings are critical for ensuring road user safety. To improve their safety, there are even different types of road lane markings, such as single solid lines, double solid lines, dashed single line etc. Their colors generally are white and yellow. This road lane markings mainly used to provide guidance and information for road user to comply with the rule of the road. Unfortunately, these markings get worn out with time and may even disappear. In order to prevent this from happening, regular inspection and maintenance need to be conducted. Manual inspection is tedious, slow, and prone to human errors. With the recent technological advancement, especially in machine vision and artificial intelligence, automated or semi-automated missing road lane marking detection systems can potentially be developed. In this work, preliminary study of the implementation of one of the latest deep learning algorithms, i.e. YOLOv5, has been carried out in the detection and classification of missing road lane markings. This paper shows the preliminary results which look promising as the mean Average Precision (mAP@0.5) reaches 0.995.
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Golubenko, V. V., A. A. Aleksandrov, and V. V. Sirotyuk. "ANALYSIS OF PREDICTION METHODS FOR THE FUNCTIONAL DURABILITY OF ROAD MARKINGS." Vestnik SibADI 15, no. 4 (September 12, 2018): 574–87. http://dx.doi.org/10.26518/2071-7296-2018-4-574-587.
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Introduction. The actuality of the research is emphasized in the Strategy of proclaiming the desire for zero mortality on the country’s roads. Increasing the functional durability (the service life period) of horizontal road markings is an effective and cheap way to organize the movement of vehicles and pedestrians, which could reduce the number of accidents by 15-30%.Methods. The detailed factor analysis, influenced on the functional durability of the horizontal road markings and on the durability forecasting methods, is made by the authors.Results. The authors have established the main factors determining the functional durability of the horizontal road marking. They are divided into five groups: weather-climatic; mechanical; properties of the marking material; technological; properties of the road pavement. Moreover, the article presents a critical analysis of the existing methods of forecasting the functional durability of horizontal marking. In foreign publications on the issue under consideration there is no work on the prediction of the functional durability of horizontal marking by creating complex mathematical models. Most foreign and Russian models are based on empirical dependences. However, these methods and models do not take into account a number of important factors, such as the degree of roughness and abrasion of stone materials, their embedding in asphalt concrete coating, etc.Disscussion and conclusion. The authors came to the conclusion that attempts to create a single reliable mathematical model that takes into account more than 40 factors couldn’t be realistic. Therefore, there is a task of developing a number of models that allow to have greater extent that take into account the properties of the road surface, the location and variety of marking lines and predict the functional durability of horizontal road markings for different types of road pavement more reliably.
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Bwire, Hanniabl, and Joseph K. Mnkeni. "Auditing of Traffic Signs and Road Markings in Dar es Salaam City." Tanzania Journal of Engineering and Technology 32, no. 1 (June 30, 2009): 35–53. http://dx.doi.org/10.52339/tjet.v32i1.442.
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This study examines the efficacy of the traffic signs and road markings in Dar es Salaam City. The studyfocuses on the availability and conditions of traffic signs and road markings in some selected roads in the city.Auditing of traffic signs and road markings is carried out as one of the means of promoting road safety andimproving road capacity in the city. The auditing procedure involved field studies through the use of trafficsigns/markings checklists and ranking of road stretches according to the availability and conditions of trafficsigns observed for improvement prioritization in case of financial constraints. In ranking the road sections in accordance to the overall conditions and availability of traffic signs, an index was developed. The index considered six factors of traffic signs availability and conditions that were observed in the field; (i) availability (ii) orrectness of size (iii) visibility of signs (iv) physical condition (v) correct placement and (vi) the need of sign protection. It is recommended that the responsible agency and Municipal councils carry out auditing f traffic signs/markings and identify deficiencies in signage and road markings and plan for improvement accordingly. In case of financial constraints, the developed performance index may be used to rank roads/road sections for improvement priority.
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Dewi, Christine, Rung-Ching Chen, Yong-Cun Zhuang, and Henoch Juli Christanto. "Yolov5 Series Algorithm for Road Marking Sign Identification." Big Data and Cognitive Computing 6, no. 4 (December 7, 2022): 149. http://dx.doi.org/10.3390/bdcc6040149.
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Road markings and signs provide vehicles and pedestrians with essential information that assists them to follow the traffic regulations. Road surface markings include pedestrian crossings, directional arrows, zebra crossings, speed limit signs, other similar signs and text, and so on, which are usually painted directly onto the road surface. Road markings fulfill a variety of important functions, such as alerting drivers to the potentially hazardous road section, directing traffic, prohibiting certain actions, and slowing down. This research paper provides a summary of the Yolov5 algorithm series for road marking sign identification, which includes Yolov5s, Yolov5m, Yolov5n, Yolov5l, and Yolov5x. This study explores a wide range of contemporary object detectors, such as the ones that are used to determine the location of road marking signs. Performance metrics monitor important data, including the quantity of BFLOPS, the mean average precision (mAP), and the detection time (IoU). Our findings shows that Yolov5m is the most stable method compared to other methods with 76% precision, 86% recall, and 83% mAP during the training stage. Moreover, Yolov5m and Yolov5l achieve the highest score, mAP 87% on average in the testing stage. In addition, we have created a new dataset for road marking signs in Taiwan, called TRMSD.
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Zhao, Luhua, Haonan Ding, Junjing Sun, Guangna Wu, Huiyao Xing, Wei Wang, and Jie Song. "Prediction of Service Life of Thermoplastic Road Markings on Expressways." Sustainability 15, no. 21 (October 25, 2023): 15237. http://dx.doi.org/10.3390/su152115237.
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Currently, historical data and on-site surveys—particularly in the context of China—are heavily relied upon to determine the best time to maintain expressway road markings. This study aims to determine what influences the service life of thermoplastic road markings on expressways in Shandong Province, China, while considering both those motorways’ unique characteristics and the local environment. Additionally, a scientific evaluation of the road markings’ retroreflective coefficient’s decay pattern will be undertaken. We collected the retroreflective data for twelve consecutive months regarding the thermoplastic road markings on five expressways and potential influencing factors such as age of marking and annual average daily traffic. The service life of the markings was forecast using a multiple linear regression. Dominance analysis was used to quantitatively analyze each explanatory factor’s impact on the service life of the markings, and statistically significant variables were also found. Using LightGBM, a machine learning technique, a nonparametric prediction model was also created based on examining the relevance of influencing elements. The modeling results show that LightGBM generates an R2 of 0.942, implying that it offers better interpretability and higher accuracy than the regression-based approach. Additionally, LightGBM outperforms MLR according to final validation accuracies, with a score of 95.02% or more than 8% that of MLR. The results are useful for expressway marking upkeep and for driving safety.
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Storsæter, Ane Dalsnes, Kelly Pitera, and Edward McCormack. "Using ADAS to Future-Proof Roads—Comparison of Fog Line Detection from an In-Vehicle Camera and Mobile Retroreflectometer." Sensors 21, no. 5 (March 3, 2021): 1737. http://dx.doi.org/10.3390/s21051737.
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Pavement markings are used to convey positioning information to both humans and automated driving systems. As automated driving is increasingly being adopted to support safety, it is important to understand how successfully sensor systems can interpret these markings. In this effort, an in-vehicle lane departure warning system was compared to data collected simultaneously from an externally mounted mobile retroreflectometer. The test, performed over 200 km of driving on three different routes in variable lighting conditions and road classes found that, depending on conditions, the retroreflectometer could predict whether the car’s lane departure systems would detect markings in 92% to 98% of cases. The test demonstrated that automated driving systems can be used to monitor the state of pavement markings and can provide input on how to design and maintain road infrastructure to support automated driving features. Since data about the condition of lane marking from multiple lane departure warning systems (crowd-sourced data) can provide input into the pavement marking management systems operated by many road owners, these findings also indicate that these automated driving sensors have an important role in enhancing the maintenance of pavement markings.
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Dewi, Christine, Rung-Ching Chen, Yong-Cun Zhuang, Xiaoyi Jiang, and Hui Yu. "Recognizing Road Surface Traffic Signs Based on Yolo Models Considering Image Flips." Big Data and Cognitive Computing 7, no. 1 (March 22, 2023): 54. http://dx.doi.org/10.3390/bdcc7010054.
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In recent years, there have been significant advances in deep learning and road marking recognition due to machine learning and artificial intelligence. Despite significant progress, it often relies heavily on unrepresentative datasets and limited situations. Drivers and advanced driver assistance systems rely on road markings to help them better understand their environment on the street. Road markings are signs and texts painted on the road surface, including directional arrows, pedestrian crossings, speed limit signs, zebra crossings, and other equivalent signs and texts. Pavement markings are also known as road markings. Our experiments briefly discuss convolutional neural network (CNN)-based object detection algorithms, specifically for Yolo V2, Yolo V3, Yolo V4, and Yolo V4-tiny. In our experiments, we built the Taiwan Road Marking Sign Dataset (TRMSD) and made it a public dataset so other researchers could use it. Further, we train the model to distinguish left and right objects into separate classes. Furthermore, Yolo V4 and Yolo V4-tiny results can benefit from the “No Flip” setting. In our case, we want the model to distinguish left and right objects into separate classes. The best model in the experiment is Yolo V4 (No Flip), with a test accuracy of 95.43% and an IoU of 66.12%. In this study, Yolo V4 (without flipping) outperforms state-of-the-art schemes, achieving 81.22% training accuracy and 95.34% testing accuracy on the TRMSD dataset.
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Kim, Jin Gon, Dong Yeob Han, Ki Yun Yu, Yong Il Kim, and Sung Mo Rhee. "Efficient extraction of road information for car navigation applications using road pavement markings obtained from aerial images." Canadian Journal of Civil Engineering 33, no. 10 (October 1, 2006): 1320–31. http://dx.doi.org/10.1139/l06-069.
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The efficient extraction of road information is increasingly important with the rapid growth of road-related services, such as car navigation systems, telematics, and location-based services. Conventional methods of creating and updating road information are expensive and time consuming. Therefore, a set of processes is required that collects the same information more efficiently. We propose a new method for collecting road information in complex urban areas from road pavement markings located on aerial images. This information includes lane and symbol markings that guide direction; the geometric properties of the pavement markings and their spatial relationships are analyzed. Road construction manuals and a series of cutting-edge techniques, including template matching, are used in our analysis. To validate our approach, the accuracy of our results was evaluated by comparing the data with manually extracted ground truth data. Our approach demonstrates that road information can be extracted efficiently to an extent in a complex urban area.Key words: aerial image, automatic extraction, pavement marking, road information, CNS.
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Vasiliauskas, Ignas, and Audrius Vaitkus. "STUDY OF ROAD AND STREET HORIZONTAL MARKING REFLECTIVITY." Mokslas - Lietuvos ateitis 12 (October 1, 2020): 1–5. http://dx.doi.org/10.3846/mla.2020.13069.
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The article describes the change in the reflection of the horizontal sign examined in the scientific work taking into account the factors of road maintenance and traffic intensity. Methods applied in accordance with the standards in force in the European Union. In order to carry out the study in a smooth way, the scientific work analyzes the horizontal amount of road marking materials, types of markings, the change of reflection in the main traffic conditions. The current legal regulation of Lithuania and other European countries, the USA is also reviewed. “The research work also develops the topic of the effectiveness of the methodology of maintenance and renewal of horizontal road markings abroad and in the Republic of Lithuania.” Recommendations are provided on how to improve the quality of horizontal road marking maintenance. Analyzes of national legislation on horizontal vehicle signs, recommendations on how to improve them to ensure the quality of the production of larger horizontal signs, or the best traffic conditions for all road users. Evaluating the reflectivity of selected streets in dry and wet conditions, making suggestions on how to improve the horizontal reflections of road markings and, at the same time, traffic safety.
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Bazhynov, Olexiy, Mikhail Kravtsov, Grigory Taran, Dmytro Kudin, and Yevhen Haiek. "Plasmochemical air cleaning system in vehicle cabin." Bulletin of Kharkov National Automobile and Highway University, no. 98 (November 29, 2022): 144. http://dx.doi.org/10.30977/bul.2219-5548.2022.98.0.144.
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Problem. Currently, the problem of energy independence is one of the most urgent. Consequently, there is a significant need for alternative sources of power. Along with this, throughout the history of human development, the issues of roads and their quality have been very topical and relevant. It is especially true for Ukraine. With the development of science and technology, these issues are expanded by functionality of roads, their safety both for drivers and for other road users and the ability to perform several functions simultaneously, for example, to provide transport traffic and generate electricity. Goal. The goal is development of the method of road marking and automatic control of the road traffic using multifunctional road panels. Methodology. The analytical methods of research on the development and application of methods and devices for transforming the energy of the sun into electricity were used. Methods of mathematical modulation were used when considering the principle of multi-functional road panels, combined in one system. Results. The functional features of multi-functional road panels of pavement are revealed and their scheme on the roadway is presented. The principle of operation of the road as a unified system of multifunctional road surface panels has been researched. The scheme of implementation of road marking and automatic control of road traffic is presented. Originality. It is suggested to use special plates – multifunctional road panels – instead of the top layer of the road cover. Such an upper layer of road clothing will allow the road, as an integral complex system, to perform several functions simultaneously: to be a part of the road for motor transport, to generate electricity and to draw a road marking independently, and if necessary – to change it. Practical value. The use of the proposed method of road markings and automatic control of road traffic will make possible quality performance and rapid change of road markings at any time of the year and day, depending on the current natural and traffic conditions. Also, the proposed road pavement will generate electricity as an alternative renewable source of energy.
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Nurmuhametov, R. N., L. V. Volkova, N. I. Kunavin, and V. G. Klimenko. "Application of luminescent materials for road signs and road markings." Izvestiya MGTU MAMI 1, no. 2 (January 20, 2007): 199–203. http://dx.doi.org/10.17816/2074-0530-69718.
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The composite materials based on polyepoxy containing various luminophors are obtained. Such compositions exceed in brightness the polymer marking materials with bloomless ingridience at the expense of the energy transformation of ultraviolet and short-wave visible radiation in the long-wavelength (green-yellow or red) glow. The paper shows the prospects of application of composite materials for fluorescent composite materials for highways markings and road signs.
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Kang, Kyubyung, Donghui Chen, Cheng Peng, Dan Koo, Taewook Kang, and Jonghoon Kim. "Development of an Automated Visibility Analysis Framework for Pavement Markings Based on the Deep Learning Approach." Remote Sensing 12, no. 22 (November 23, 2020): 3837. http://dx.doi.org/10.3390/rs12223837.
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Pavement markings play a critical role in reducing crashes and improving safety on public roads. As road pavements age, maintenance work for safety purposes becomes critical. However, inspecting all pavement markings at the right time is very challenging due to the lack of available human resources. This study was conducted to develop an automated condition analysis framework for pavement markings using machine learning technology. The proposed framework consists of three modules: a data processing module, a pavement marking detection module, and a visibility analysis module. The framework was validated through a case study of pavement markings training data sets in the U.S. It was found that the detection model of the framework was very precise, which means most of the identified pavement markings were correctly classified. In addition, in the proposed framework, visibility was confirmed as an important factor of driver safety and maintenance, and visibility standards for pavement markings were defined.
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NIE, YIMING, BIN DAI, XIANGJING AN, ZHENPING SUN, TAO WU, and HANGEN HE. "FAST LANE DETECTION USING DIRECTION KERNEL FUNCTION." International Journal of Wavelets, Multiresolution and Information Processing 10, no. 02 (March 2012): 1250017. http://dx.doi.org/10.1142/s0219691312500178.
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The lane information is essential to the highway intelligent vehicle applications. The direct description of the lanes is lane markings. Many vision methods have been proposed for lane markings detection. But in practice there are some problems to be solved by previous lane tracking systems such as shadows on the road, lighting changes, characters on the road and discontinuous changes in road types. Direction kernel function is proposed for robust detection of the lanes. This method focuses on selecting points on the markings edge by classification. During the classifying, the vanishing point is selected and the parts of the lane marking could form the lanes. The algorithm presented in this paper is proved to be both robust and fast by a large amount of experiments in variable occasions, besides, the algorithm can extract the lanes even in some parts of lane markings missing occasions.
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Lagahit, Miguel Luis R., and Masashi Matsuoka. "Focal Combo Loss for Improved Road Marking Extraction of Sparse Mobile LiDAR Scanning Point Cloud-Derived Images Using Convolutional Neural Networks." Remote Sensing 15, no. 3 (January 19, 2023): 597. http://dx.doi.org/10.3390/rs15030597.
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Road markings are reflective features on roads that provide important information for safe and smooth driving. With the rise of autonomous vehicles (AV), it is necessary to represent them digitally, such as in high-definition (HD) maps generated by mobile mapping systems (MMSs). Unfortunately, MMSs are expensive, paving the way for the use of low-cost alternatives such as low-cost light detection and ranging (LiDAR) sensors. However, low-cost LiDAR sensors produce sparser point clouds than their survey-grade counterparts. This significantly reduces the capabilities of existing deep learning techniques in automatically extracting road markings, such as using convolutional neural networks (CNNs) to classify point cloud-derived imagery. A solution would be to provide a more suitable loss function to guide the CNN model during training to improve predictions. In this work, we propose a modified loss function—focal combo loss—that enhances the capability of a CNN to extract road markings from sparse point cloud-derived images in terms of accuracy, reliability, and versatility. Our results show that focal combo loss outperforms existing loss functions and CNN methods in road marking extractions in all three aspects, achieving the highest mean F1-score and the lowest uncertainty for the two distinct CNN models tested.
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Babić, Darko, Dario Babić, Mario Fiolić, Arno Eichberger, and Zoltan Ferenc Magosi. "Impact of Road Marking Retroreflectivity on Machine Vision in Dry Conditions: On-Road Test." Sensors 22, no. 4 (February 9, 2022): 1303. http://dx.doi.org/10.3390/s22041303.
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(1) Background: Due to its high safety potential, one of the most common ADAS technologies is the lane support system (LSS). The main purpose of LSS is to prevent road accidents caused by road departure or entrance in the lane of other vehicles. Such accidents are especially common on rural roads during nighttime. In order for LSS to function properly, road markings should be properly maintained and have an adequate level of visibility. During nighttime, the visibility of road markings is determined by their retroreflectivity. The aim of this study is to investigate how road markings’ retroreflectivity influences the detection quality and the view range of LSS. (2) Methods: An on-road investigation comprising measurements using Mobileye and a dynamic retroreflectometer was conducted on four rural roads in Croatia. (3) Results: The results show that, with the increase of markings’ retroreflection, the detection quality and the range of view of Mobileye increase. Additionally, it was determined that in “ideal” conditions, the minimal value of retroreflection for a minimum level 2 detection should be above 55 mcd/lx/m2 and 88 mcd/lx/m2 for the best detection quality (level 3). The results of this study are valuable to researchers, road authorities and policymakers.
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Ma, H., Z. Pei, Z. Wei, and R. Zhong. "AUTOMATIC EXTRACTION OF ROAD MARKINGS FROM MOBILE LASER SCANNING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 13, 2017): 825–30. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-825-2017.
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Road markings as critical feature in high-defination maps, which are Advanced Driver Assistance System (ADAS) and self-driving technology required, have important functions in providing guidance and information to moving cars. Mobile laser scanning (MLS) system is an effective way to obtain the 3D information of the road surface, including road markings, at highway speeds and at less than traditional survey costs. This paper presents a novel method to automatically extract road markings from MLS point clouds. Ground points are first filtered from raw input point clouds using neighborhood elevation consistency method. The basic assumption of the method is that the road surface is smooth. Points with small elevation-difference between neighborhood are considered to be ground points. Then ground points are partitioned into a set of profiles according to trajectory data. The intensity histogram of points in each profile is generated to find intensity jumps in certain threshold which inversely to laser distance. The separated points are used as seed points to region grow based on intensity so as to obtain road mark of integrity. We use the point cloud template-matching method to refine the road marking candidates via removing the noise clusters with low correlation coefficient. During experiment with a MLS point set of about 2 kilometres in a city center, our method provides a promising solution to the road markings extraction from MLS data.
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Babić, Darko, Dario Babić, Mario Fiolic, and Marija Ferko. "Road Markings and Signs in Road Safety." Encyclopedia 2, no. 4 (October 12, 2022): 1738–52. http://dx.doi.org/10.3390/encyclopedia2040119.
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Due to the dynamic nature and complexity of road traffic, road safety is one of the most demanding social challenges. Therefore, contemporary road safety strategies incorporate a multidisciplinary and comprehensive approaches to address this problem and improve the safety of each individual element, i.e., the human, vehicle, and road. Traffic control devices are an important part of road infrastructure, among which road markings and road signs play a significant role. In general, road markings and signs represent basic means of communication between the road authorities and road users and, as such, provide road users with necessary information about the rules, warnings, obligations, and other information related to the upcoming situations and road alignment. The aim of this entry is to briefly present the main functions and characteristics of road markings and signs, and their role in road safety. In addition, practical issues and future trends and directions regarding road markings and signs are discussed.
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Kozłowska, Małgorzata Klaudia. "Consistency and certainty of the road marking system as a subject of protection based on the offence law. Analysis of the characteristics of the offence from article 85 § 1 of offence code." Transportation Overview - Przeglad Komunikacyjny 2017, no. 1 (January 1, 2017): 17–23. http://dx.doi.org/10.35117/a_eng_17_01_03.
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Nowadays when the road infrastructure rapidly expands as well as the traffic, the correct road markings are of a vital importance in ensuring safety and efficiency of this traffic. Negligible number of road incidents caused by incorrect road markings results in treating quality and certainty of those markings as being of less importance. Thus, such an important issue is to ensure effective, criminal law protection of the legal interests which is a stable and reliable system of road markings. Polish legislator adopted as a subject of individual protection on the basis of code of offence inviolability of road signs and signals, and what stands behind it - stability and certainty of the road markings system. Road markings; Inviolability of road marks and signals; Road infrastructure
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Yang, Ronghao, Qitao Li, Junxiang Tan, Shaoda Li, and Xinyu Chen. "Accurate Road Marking Detection from Noisy Point Clouds Acquired by Low-Cost Mobile LiDAR Systems." ISPRS International Journal of Geo-Information 9, no. 10 (October 20, 2020): 608. http://dx.doi.org/10.3390/ijgi9100608.
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Road markings that provide instructions for unmanned driving are important elements in high-precision maps. In road information collection technology, multi-beam mobile LiDAR scanning (MLS) is currently adopted instead of traditional mono-beam LiDAR scanning because of the advantages of low cost and multiple fields of view for multi-beam laser scanners; however, the intensity information scanned by multi-beam systems is noisy and current methods designed for road marking detection from mono-beam point clouds are of low accuracy. This paper presents an accurate algorithm for detecting road markings from noisy point clouds, where most nonroad points are removed and the remaining points are organized into a set of consecutive pseudo-scan lines for parallel and/or online processing. The road surface is precisely extracted by a moving fitting window filter from each pseudo-scan line, and a marker edge detector combining an intensity gradient with an intensity statistics histogram is presented for road marking detection. Quantitative results indicate that the proposed method achieves average recall, precision, and Matthews correlation coefficient (MCC) levels of 90%, 95%, and 92%, respectively, showing excellent performance for road marking detection from multi-beam scanning point clouds.
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Liu, Tao, Wenhao Yao, Rui He, and Xiamang Wang. "The influence of vehicle rolling on the retroreflective coefficient of hot melt markings." Vibroengineering Procedia 50 (September 21, 2023): 200–205. http://dx.doi.org/10.21595/vp.2023.23437.
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In the service life of traffic markings, the visibility of the markings can be significantly impacted due to continuous rolling by vehicles. This study aims to investigate the change of retroreflective coefficient of traffic markings at different positions on the road after being rolled by vehicles. A two-month follow-up study was conducted on a specific section of the road, focusing on the sideline and middle line of the lane. The retroreflective coefficient instrument was utilized to collect data on the retroreflective coefficient of the markings. Results revealed that the middle line, due to more frequent vehicle rolling, exhibited a larger range in its retroreflective coefficient as compared to the sideline of the lane. The change in reflection ability of hot melt markings, when rolled by the vehicle, depends on the mosaic condition of the glass beads within the coating. It was observed that the rolling of the vehicle had a direct impact on the retroreflective coefficient of the hot melt marking line. Furthermore, it was found that with increased rolling of the marking line, the greater the impact on its reflection capacity and the thickness also demonstrated a significant decrease.
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Shaw, John W., Madhav V. Chitturi, and David A. Noyce. "Special-Color Pavement Marking for Highway Work Zones: Literature Review of International Practices." Transportation Research Record: Journal of the Transportation Research Board 2617, no. 1 (January 2017): 78–86. http://dx.doi.org/10.3141/2617-10.
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Roadway lanes are often repositioned to accommodate highway work operations; as a result, pavement markings need to be altered. Although there are various methods for removing or obscuring existing pavement markings, “ghost” markings often remain at the locations of the old lane lines. These ghost markings can be quite conspicuous under certain lighting conditions, creating the potential for road user confusion. The Canadian province of Ontario and several European countries routinely use a special marking color (orange or yellow) to increase the salience of temporary lane lines. Special-color markings have also been used experimentally in Australia; New Zealand; Quebec City, Canada; and the United States. As a first step toward identifying the benefits and risks of special-color markings, existing practices from several countries are reviewed and summarized. The review identified a significant policy difference among jurisdictions: in some jurisdictions special-color markings override existing markings (so that the old markings are left in place), whereas other jurisdictions use special-color temporary marking but also attempt to remove old lane lines. The recent special-color marking demonstration projects in Australia, Canada, New Zealand, and the United States have been on major freeways, but European practice suggests that special-color marking could have significant benefit for urban arterial streets.
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Zhang, Fangzhi, Yue Xie, Xiaokang Zhao, Yinzhang He, Jianzhong Pei, Yuanhe Xing, Shaobo Wang, and Jiupeng Zhang. "Aluminate Long Afterglow Luminescent Materials in Road Marking Field Research Progress and Development: A Review." Buildings 14, no. 7 (July 12, 2024): 2152. http://dx.doi.org/10.3390/buildings14072152.
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This paper reviews the research progress and development of aluminate long afterglow luminescent materials in the field of road marking, especially the study of rare earth ion-activated strontium aluminate (SrAl2O4: Eu2+, Dy3+)-based long afterglow powders. This article begins by describing the importance of road markings and the need to improve their visibility and durability at night and in adverse weather conditions. Subsequently, the current passive and active methods for improving the visibility of marking materials are discussed in detail, focusing on the advantages of aluminate long afterglow materials and challenges related to their hydrolysis and thermal stability. Through the application of organic–inorganic composite coating technology, the water resistance and thermal stability of the materials can be improved, thus enhancing the performance of road markings. This article also summarizes the current research status of different types of long afterglow road marking coatings. It analyzes the luminescence mechanism of aluminate long afterglow materials. Additionally, this article discusses future research directions and application prospects. The aim is to provide technical references and support for the wide application of long afterglow self-luminous road marking coatings.
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Babić, Dario, Mario Fiolić, Darko Babić, and Timothy Gates. "Road Markings and Their Impact on Driver Behaviour and Road Safety: A Systematic Review of Current Findings." Journal of Advanced Transportation 2020 (August 28, 2020): 1–19. http://dx.doi.org/10.1155/2020/7843743.
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As part of the traffic control plan, road markings form the traffic surface and provide visual guidance for road users. Since their first application to the present day, road markings have become a common element of road infrastructure and one of the basic low-cost safety measures. The aim of this paper is to provide a systematic review of the most significant academic activities to date regarding the influence of longitudinal and transverse road markings as well as road markings for hazard locations (curves, intersections, and rural-urban transitions) on driver’s behaviour and overall road safety. The review includes a total of 71 studies from which are 52 peer-reviewed journal studies, 4 conference proceedings, and 15 professional reports. The studies are, based on their aim, divided into two categories: (1) studies on the impact of road markings on driver behaviour (36 studies) and (2) studies on the impact of road markings on road safety (35 studies).
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Adeyemi, Oladimeji, Martins Irhebhude, and Adeola Kolawole. "Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques." Advances in Image and Video Processing 7, no. 5 (November 8, 2019): 30–42. http://dx.doi.org/10.14738/aivp.75.7205.
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This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.
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Xu, Shuyuan, Jun Wang, Peng Wu, Wenchi Shou, Xiangyu Wang, and Mengcheng Chen. "Vision-Based Pavement Marking Detection and Condition Assessment—A Case Study." Applied Sciences 11, no. 7 (April 1, 2021): 3152. http://dx.doi.org/10.3390/app11073152.
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Pavement markings constitute an effective way of conveying regulations and guidance to drivers. They constitute the most fundamental way to communicate with road users, thus, greatly contributing to ensuring safety and order on roads. However, due to the increasingly extensive traffic demand, pavement markings are subject to a series of deterioration issues (e.g., wear and tear). Markings in poor condition typically manifest as being blurred or even missing in certain places. The need for proper maintenance strategies on roadway markings, such as repainting, can only be determined based on a comprehensive understanding of their as-is worn condition. Given the fact that an efficient, automated and accurate approach to collect such condition information is lacking in practice, this study proposes a vision-based framework for pavement marking detection and condition assessment. A hybrid feature detector and a threshold-based method were used for line marking identification and classification. For each identified line marking, its worn/blurred severity level was then quantified in terms of worn percentage at a pixel level. The damage estimation results were compared to manual measurements for evaluation, indicating that the proposed method is capable of providing indicative knowledge about the as-is condition of pavement markings. This paper demonstrates the promising potential of computer vision in the infrastructure sector, in terms of implementing a wider range of managerial operations for roadway management.
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Coves-Campos, Andrés, Luis Bañón, José Coves-García, and Salvador Ivorra. "In Situ Study of Road Marking Durability Using Glass Microbeads and Antiskid Aggregates as Drop-On Materials." Coatings 8, no. 10 (October 21, 2018): 371. http://dx.doi.org/10.3390/coatings8100371.
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Road markings play an important role in road safety because they provide significant information to drivers about the road. For that reason, they must be replaced when they are not correctly perceived by road users. To analyse which are the main factors that affect road marking perception over time, a test section was designed in a two-lane rural highway, running actual traffic over 18 different types of markings fabricated with different combinations of drop-on materials. Chromatic coordinates, luminance, and retroreflectivity of each sample were measured during 18 months in order to study their evolution over time. The results obtained show different behaviours depending on the aggregates and application method used. An increment of the durability has been observed with the use of different layers and mixtures of glass microbeads with different sizes.
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Franco, Felipe, Max Mauro Dias Santos, Rui Tadashi Yoshino, Leopoldo Rideki Yoshioka, and João Francisco Justo. "ROADLANE—The Modular Framework to Support Recognition Algorithms of Road Lane Markings." Applied Sciences 11, no. 22 (November 15, 2021): 10783. http://dx.doi.org/10.3390/app112210783.
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One of the main actions of the driver is to keep the vehicle in a road lane within its markings, which could be aided with modern driver-assistance systems. Forward digital cameras in vehicles allow deploying computer vision strategies to extract the road recognition characteristics in real-time to support several features, such as lane departure warning, lane-keeping assist, and traffic recognition signals. Therefore, the road lane marking needs to be recognized through computer vision strategies providing the functionalities to decide on the vehicle’s drivability. This investigation presents a modular architecture to support algorithms and strategies for lane recognition, with three principal layers defined as pre-processing, processing, and post-processing. The lane-marking recognition is performed through statistical methods, such as buffering and RANSAC (RANdom SAmple Consensus), which selects only objects of interest to detect and recognize the lane markings. This methodology could be extended and deployed to detect and recognize any other road objects.
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Bøgesvang Basse, Henrik. "Pioneering Safety, Marking the Future." Traffic Technology International 2024, no. 1 (January 2024): 92–93. http://dx.doi.org/10.12968/s1356-9252(24)40043-9.
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Hoang, Toan, Phong Nguyen, Noi Truong, Young Lee, and Kang Park. "Deep RetinaNet-Based Detection and Classification of Road Markings by Visible Light Camera Sensors." Sensors 19, no. 2 (January 11, 2019): 281. http://dx.doi.org/10.3390/s19020281.
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Detection and classification of road markings are a prerequisite for operating autonomous vehicles. Although most studies have focused on the detection of road lane markings, the detection and classification of other road markings, such as arrows and bike markings, have not received much attention. Therefore, we propose a detection and classification method for various types of arrow markings and bike markings on the road in various complex environments using a one-stage deep convolutional neural network (CNN), called RetinaNet. We tested the proposed method in complex road scenarios with three open datasets captured by visible light camera sensors, namely the Malaga urban dataset, the Cambridge dataset, and the Daimler dataset on both a desktop computer and an NVIDIA Jetson TX2 embedded system. Experimental results obtained using the three open databases showed that the proposed RetinaNet-based method outperformed other methods for detection and classification of road markings in terms of both accuracy and processing time.
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Jeong, Siwoo, Jonghyeon Ko, Sukki Lee, Jihoon Kang, Yeni Kim, Soon Yong Park, and Sungchul Mun. "Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles." Sensors 24, no. 8 (April 13, 2024): 2505. http://dx.doi.org/10.3390/s24082505.
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The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS’s recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles.
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Yao, L., Q. Chen, C. Qin, H. Wu, and S. Zhang. "AUTOMATIC EXTRACTION OF ROAD MARKINGS FROM MOBILE LASER-POINT CLOUD USING INTENSITY DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 2113–19. http://dx.doi.org/10.5194/isprs-archives-xlii-3-2113-2018.
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With the development of intelligent transportation, road’s high precision information data has been widely applied in many fields. This paper proposes a concise and practical way to extract road marking information from point cloud data collected by mobile mapping system (MMS). The method contains three steps. Firstly, road surface is segmented through edge detection from scan lines. Then the intensity image is generated by inverse distance weighted (IDW) interpolation and the road marking is extracted by using adaptive threshold segmentation based on integral image without intensity calibration. Moreover, the noise is reduced by removing a small number of plaque pixels from binary image. Finally, point cloud mapped from binary image is clustered into marking objects according to Euclidean distance, and using a series of algorithms including template matching and feature attribute filtering for the classification of linear markings, arrow markings and guidelines. Through processing the point cloud data collected by RIEGL VUX-1 in case area, the results show that the F-score of marking extraction is 0.83, and the average classification rate is 0.9.
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Rassokha, V. I., and N. A. Nikitin. "ESTIMATION OF THE EFFECTIVENESS OF SPIRAL MARKINGS AT RING ROAD INTERSECTIONS." Intellect. Innovations. Investments, no. 2 (2023): 130–41. http://dx.doi.org/10.25198/2077-7175-2023-2-130.
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The results of a number of foreign studies recommend the application of spiral markings to ensure a higher level of safety at roundabouts of medium and large diameter highways. In a number of cities of the Russian Federation, these recommendations were implemented with the introduction of new traffic management schemes using spiral marking of traffic lanes. However, there is no objective data confirming the effectiveness of these measures in Russia. The aim of the study was to evaluate the effectiveness of spiral markings according to two criteria – the number of road accidents and traffic violations at roundabouts before and after the reorganization of traffic. The information was obtained from the traffic police and from video surveillance of individual sections of roundabouts. As a result of the study: 1) the hypothesis of a decrease in the number of accidents and the assertion that new traffic management schemes (spiral markings) by themselves provide greater traffic safety at roundabouts are refuted; 2) it is revealed that drivers tend to violate the requirements of spiral markings, which increases the likelihood of accidents; 3) it is concluded that new traffic management schemes can have a positive impact on road safety only when engineering measures are implemented to physically separate traffic flows on the ring roadway and reduce the speed of vehicles at the entrances and exits of roundabouts. The results of the work revealed promising areas for further research: to study the relationship between the trajectory of vehicles, their location from the point of view of certain intersection sites, spiral markings, characteristics of traffic flow (speed, density, congestion, flow composition), as well as weather condi¬tions. The identification of such links will allow us to evaluate the effectiveness of spiral marking and draw conclusions about the expediency of its further use at roundabouts in the territory of the Russian Federation.
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XU, YUANYUAN, BIN KONG, HU WEI, and QIANG TIAN. "LANE-BASED DIRECTION MARKING RECOGNITION USING HU MOMENTS." International Journal of Information Acquisition 09, no. 03n04 (September 2013): 1350016. http://dx.doi.org/10.1142/s0219878913500162.
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In intelligent vehicle system, it is significant to detect and identify road markings for vehicles to follow traffic regulation. This paper proposes a method to recognize direction markings on road surface, which is on the basis of detected lanes and uses Hu moments. First of all, the detection of lanes is based on horizontal luminance difference, which converts the RGB color image to the luminance image, calculates the horizontal luminance difference, obtains the candidate points of lanes' edge and uses least square method to fit the lanes. Secondly, with the detected lines as guide for the search of candidate marking, the paper extracts Hu moments of candidate marking, calculates its Mahalanobis distance to every marking type and classifies it to the type which has the minimal distance with the candidate marking. From the simulation results, the method to detect lanes is more effective and time-efficient than canny or sobel edge detection methods; the method to recognize direction marking is effective and has a high accuracy.
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Yao, L., C. Qin, Q. Chen, H. Wu, and S. Zhang. "AUTOMATIC EXTRACTION AND RECOGNITION OF ROAD MARKINGS BASED ON VEHICLE LASER POINT CLOUD." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 313–19. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-313-2020.
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Abstract. At present, automatic driving technology has become one of the development direction of the future intelligent transportation system. The high high-precision map, which is an important supplement of the on on-board sensors under the condition of shielding or the restriction of observation distance, provides a priori information for high high-precision positioning and path planning of the automatic driving with the level of L3 and above. The position and semantic information of the road markings, such as the absolute coordinates of th e solid line and the bro ken line, are the basic components of the high high-precision map. At present, point cloud data are still one of the most important data source of the high high-precision map. So, how to get road markings information from original point clouds automatically deserve study. In this paper, point cloud is sliced by the mileage of the road, then each slice is projected onto respective vertical section section. Random Sample Consensus (RANSAC) algorithm is applied to establish road surface buffer area . Finally, moving window filtering is used to extract road surface point cloud from road surface buffer area area. On this basis, the road surface point cloud image is transformed into raster image with a certain resolution by using the method of inverse distance weighted interpolation , and the grid image is converted into binary image by using the method of adaptive threshold segmentation based on the integral graph. Then the method of the Euclidean clustering is used to extract the road markings point cloud from the binary image. Characteristic attribute detection is applied to recognize solid line marking from all clusters. Deep learning network framework pointnet++ is applied to recognize remain road markings including guideline, broken line, straight arrow, and right turn arrow.
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Duan, Weidang, Haoqin Guo, Rui He, Xiamang Wang, and Xiaosong Lu. "The relationship between initial retroreflective luminous coefficient, thickness and retroreflection attenuation of thermoplastic markings." Vibroengineering Procedia 54 (April 4, 2024): 226–31. http://dx.doi.org/10.21595/vp.2024.23962.
Full textAbstract:
This study aims to investigate the effect of thickness and initial retroreflective luminous coefficient (RL) on the retroreflection attenuation of thermoplastic road marking. The road marking with a thickness range of 2.5-3.7 mm were divided into six groups with a 0.2 mm interval to assess the relationship between the initial RL and the retroreflection attenuation. The settlement pattern of glass beads was also investigated for road markings with different thicknesses. Furthermore, the road markings with an initial RL range of 457-492 mcd·lx-1·m-2 and a thickness range of 2.46 ~ 3.42 mm were divided into four groups. The RL values at 7, 15, 30, 60 and 90 days were measured and compared to assess the impact of thickness and initial RL on retroreflection attenuation. It is concluded that the change of RL value is due to the change of glass bead embedment. When the thickness is 3.08 mm and the initial RL is 465.5 mcd·lx-1·m-2, there is a noticeable delay in the retroreflection attenuation.
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Erastus Mishengu Mwanaumo and Kelvin Lungu Agabu. "Motorist Understanding of Pavement Centre Lines and their Effect on Driving Behaviour." International Journal of Engineering and Management Research 11, no. 1 (February 13, 2021): 110–22. http://dx.doi.org/10.31033/ijemr.11.1.16.
Full textAbstract:
Human factors and more generally driver errors account for the largest number of road accidents. Driver errors are external human factors that can contribute to specific error types selected from slip, lapse, mistake and violation. Action and information retrieval errors are both examples of driver errors. The failure to interpret correctly an intended road marking’s message causes driver misunderstanding and lead to a driver error. Centre lines are examples of such markings and if misread or unrecognised may cause unintentional driver violations and unsafe driving. This study focused on the examining of driver understanding of road markings, and the influence of centre lines on their driving behaviour. This study determined that drivers had a much better understanding of the overtaking messages intended by road markings, than the directional flow message. Drivers demonstrated that they relied more on signs and other drivers to determine whether the road is a two-way or not. This study demonstrated that the presence of both centre lines and edge lines have a positive effect on a driver in handling and controlling of their vehicles’ position. It was postulated from this study that the absence of the edge lines has a more significant effect on a vehicle’s position than the absence of centre lines.