Road Markings Research Articles

НОРМАТИВНО-ПРАВОВЫЕ ОСОБЕННОСТИ ПРИМЕНЕНИЯ ДОРОЖНЫХ ЗНАКОВ 5.15.1, 5.15.2 И РАЗМЕТКИ 1.18 НА УЧАСТКАХ ДОРОГ С ДВИЖЕНИЕМ МАРШРУТНЫХ ТРАНСПОРТНЫХ СРЕДСТВ

The article deals with the problem of violations of traffic rules by bus drivers when passing intersections. Violations of different nature in the presence of road signs 5.15.1 and 5.15.2, as well as the lines of road markings 1.18 are given. There are presented specific locations in the city of Tyumen, where drivers commit intentional and unintentional violations. Court practice on the issues of offenses at intersections by a route vehicle is analyzed. Possible ways of solving this problem, including at the legislative level, are suggested.

A Method to Assess the Interpretability of Road Markings and Traffic Signs for Autonomous Vehicle Traffic

A Method to Assess the Interpretability of Road Markings and Traffic Signs for Autonomous Vehicle Traffic

Vehicle Trajectory Estimation based on Drive Recorder Data

In the present study, a method for estimating the self-position of a vehicle using data recorded by a drive recorder is investigated. In order to confirm the performance of the self-position estimation method, an experiment was conducted using a simulator. The self-position of a vehicle was estimated using image registration based on brightness between a bird's-eye view image made from a vehicle's front image and a 2D map image of the GNSS positioning location. As a result, if road boundary features were found in the bird's-eye view image, the proposed method is able to estimate the vehicle's self-position more accurately than GNSS, regardless of the existence of road markings. In addition, on straight roads with road boundary features in only one direction, the vehicle's self-position estimation was accurate enough to identify the lane in which the vehicle is currently located relative to the direction of the brightness gradient on a 2D map.

Planning of School Safety Zone (Zoss) On The Education Road Of Merauke Regency

School Safety Zone (ZOSS) One of the efforts given by the government to children's schools is to implement facilities that support the safety of pedestrians. Jalan Pendidikan is a road that is used by many vehicles because several schools can cause problems, one of which is that it can cause school children to be prone to traffic accidents. The purpose of this study was to determine thelevelof roadservice, andthe levelof pedestrians, andtoplan ZOSS on Jalan Pendidikan. The method used in this study is based on the 1997 Indonesian Road Capacity Manual (MKJI) and based on SK.3582/AJ.403/DRJD/2018. The data taken are pedestrian volume, traffic volume, vehiclespeed, and sidebarriers.Based ontheresults that canbeobtained from the level of road service on Jalan Pendidikan using the 1997 MKJI, the capacity (C) =1462 smp/hour, with a degree of saturation (DS) = 0.29 with a service level of B. From the results of the analysis of the volume of pedestrians, It can be seen that pedestrian activity is 32% during school children's entry hours and after school hours, both set and crossing the road. Based on SK.3582/AJ.403/DRJD/2018 plural ZOSS can be applied which consists of road markings and traffic signs.

Safer Road Users: Investigating the Influence of Prime Road Markings on Motorcycle Rider Behaviour

Safer Road Users: Investigating the Influence of Prime Road Markings on Motorcycle Rider Behaviour

Проблемы технического нормирования в сфере строительных материалов

To facilitate the development of the construction industry, it is necessary to timely issue new standards and to revise the existing ones. However, several problems in the field of technical regulation have been revealed associated with the unwillingness to apply scientific and practical methods in the development of standards and the lack of a hierarchical system of standardized requirements and methods of controlling them. In this regard, the fund of normative construction documents is characterized by unreasonable growth due to the presence of duplicate standards and an excessive number of identical standards for testing methods applied to homogeneous products. Also, the quality of the standards themselves is not up to date: there are ambiguous interpretations, and the requirements and methods are not pre-approved in the industry scientific organizations, which makes it difficult to implement them in the activities of various laboratories. Ultimately, his has a negative impact on the reliability of the obtained product test results and, consequently, can raise the level of risk to the impartiality of the laboratories. There are regarded the problems of standardization on examples of regulatory documents on crushed stone, curbstone, bituminous materials, materials for road markings, as well as suggested ways of solving these problems.

Modification of Yolo V4 for Road Marking Sign Recognition Based on Deep Learning

Modification of Yolo V4 for Road Marking Sign Recognition Based on Deep Learning

Vehicle Localization Using Magnetic Markers Incorporating EKF and Maximum Likelihood Estimation

Vehicle localization is an important technical factor in autonomous driving vehicles. Particularly, accurate and precise information about the vehicle position should be obtained, with robustness towards various weather and road conditions. A method using magnetic road markers as coordinate reference points for vehicle localization has proven to achieve the above requirements. However, such method has various limitations. For example, it is unable to localize the vehicle when the vehicle veers off the course of magnetic marker system. This is problematic especially in places where vehicles can take various paths, such as intersections. In this paper, we propose a concept to localize the vehicle using only one set of magnetic sensors, and a novel marker placement method, where markers are placed in a grid-like patterns to enable localization for various paths. To achieve this, extended Kalman filter was used to integrate information from inertial measurement unit with the information from magnetic markers. Maximum likelihood estimation was used to associate the coordinates of detected markers from a known marker coordinate map. Simulation using MATLAB showed that the proposed method is plausible. The proposed localization algorithm was able to correctly associate the marker coordinates and accurately estimate the vehicle position and heading in some conditions. It also suggested that the marker placement pattern could affect the precision of the marker association algorithm.

Equipment Condition for Zebra Crossing Night-Time Safety Performance in Latvia

According to road traffic accident (hereinafter referred to as RTA) statistics, the vulnerable road users are pedestrians in Latvia. The aim of this study is to investigate and analyse technical equipment used on non-signalled pedestrian crossings (zebra crossings) in Latvia and to make suggestions for measures that would increase road traffic safety on zebra crossings. RTAs involving collisions with pedestrians were filtered from the Ministry of the Interior database for a three-year period from 2016 to 2018. Thirty-two zebra crossings with a higher number of accidents with pedestrians were observed on the spot during the daylight and at night in several cities of Latvia. The main emphasis during the observation was placed on traffic signs and zebra road marking performance. Pedestrian crossings were observed from car driver’s view by taking photographs during day-time and night-time observations. Most attention was paid to road sign and road marking visibility from driver’s seat position. Retroreflection coefficient R’ was measured for each pedestrian crossing road sign. It was found that the condition and performance of traffic organisation equipment were not maintained on a regular basis and the life cycle of some traffic signs had well expired. Many road signs do not comply with minimum requirements, and road markings have weak visibility during wet weather conditions. It is recommended to improve visibility of pedestrian crossings from driver’s view in the urban areas by increasing rain vision for road markings and higher retroreflection class for traffic signs.

Phenomenological Modelling of Camera Performance for Road Marking Detection

With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination variation, appearance, and age of lane marking. In addition, the sheer infinite number of test cases for highly automated vehicles requires an increasing portion of test and validation to be performed in simulation and X-in-the-loop testing. To model the complexity of camera-based lane detection, physical models are often used, which consider the optical properties of the imager as well as image processing itself. This complexity results in high efforts for the simulation in terms of modelling as well as computational costs. This paper presents a Phenomenological Lane Detection Model (PLDM) to simulate camera performance. The innovation of the approach is the modelling technique using Multi-Layer Perceptron (MLP), which is a class of Neural Network (NN). In order to prepare input data for our neural network model, massive driving tests have been performed on the M86 highway road in Hungary. The model’s inputs include vehicle dynamics signals (such as speed and acceleration, etc.). In addition, the difference between the reference output from the digital-twin map of the highway and camera lane detection results is considered as the target of the NN. The network consists of four hidden layers, and scaled conjugate gradient backpropagation is used for training the network. The results demonstrate that PLDM can sufficiently replicate camera detection performance in the simulation. The modelling approach improves the realism of camera sensor simulation as well as computational effort for X-in-the-loop applications and thereby supports safety validation of camera-based functionality in automated driving, which decreases the energy consumption of vehicles.

Analisis Degradasi Performa Marka Jalan Thermoplastik Pada Jalan Tol

The program for maintenance and replacement of toll road surface markings aims at meeting the standards of performance set out in the Minimum Service Standards regarding for the substance of safety for road guidance indicators, which is road surface markings for the safety and comfort of toll road users. Good visibility of the road surface markings is necessary as it helps reducing the chance of traffic accidents. The problem encountered is a drastic decrease in the reflectivity level of road surface markings in a short time. The study was conducted to determine the degradation of the value of road surface markings reflectivity during the service life of the maintenance methods on the routine and periodic markers. The research was conducted experimentally by making a mock up and testing the reflectivity at the existing location using a reflectometer on 2 test models. The results showed a reflectivity degradation was 43% in the marking method carried out directly over the old road surface markings with an average reflectivity value of 401 mcd/m2/lux. This study concluded the road surface markings applied to the asphalt pavement. The thermoplastic material against the glass bead binds perfectly, while the road surface markings painted on the old road surface markings of the thermoplastic material against the glass bead do not bind properly so that the glass beads are easily detached

PERFORMANCE CHARACTERISTICS OF PAINT AND VARNISH MATERIALS FOR MARKING ROADS BASED ON LOCAL RAW MATERIALS

The aim of this work is to develop the technology of obtaining high-performance paintwork materials for road markings on the basis of local raw materials. The existing paint and varnish materials (paintwork materials) for road markings have been studied. Samples of paint and varnish materials for use as road markings were obtained under laboratory conditions. Laboratory and operational tests were conducted to determine the main quality indicators of the paint and varnish materials. As a result of monitoring the condition of road markings for two months it was found that the alkyd-based paintwork material retains its properties (color, adhesion, abrasion resistance) during the observed period and can be recommended as a coating for road markings.

Road Marking Contrast Threshold Revisited

ABSTRACT Sufficient contrast between road surface and road markings is key for a safe and comfortable driving experience. This calls for a comprehensive and well established contrast (threshold) model, which ideally results in a single contrast threshold value independent of object angular size or road luminance. The contrast threshold model introduced by Adrian is still commonly used in road lighting. More recently, new contrast metrics that also predict supra-threshold contrast visibility have been proposed, but the corresponding visibility thresholds are not yet known. In the present study, participants are presented a rendering of a highway, including road marking arrows of various size and luminance and were asked to indicate the direction of the arrow. The luminance of the road surface, acting as background for the markings, was varied too. Due to the very low luminance values and the very small differences in luminance, measurement accuracy and calibration issues require special attention. The results show good agreement with Adrian’s visibility model (R2 = 0.75) in terms of luminance contrast, background luminance and size. In addition, we used our experimental data to define contrast thresholds for several other existing image based contrast models. Unfortunately, it seems to be impossible to state one unique threshold contrast value independent of object angular size and road luminance.

RoadID: A Dedicated Deep Convolutional Neural Network for Multipavement Distress Detection

Pavement distress detection is of great significance to pavement maintenance and management. Currently, image-processing methods have been developed, especially with the widespread use of the convolutional neural networks (CNN). However, most CNN applications are still limited to the general object detection model (GODM), which is difficult to adapt to the detection of complex road scenes. In this study, a dedicated object detection model, RoadID, was designed based on the CNN to detect multiple pavement distresses in a complex environment. In terms of training data, a natural pavement distress data set was established, in which eight pavement distress types—distress, crack, patch-crack, net-crack, patch-net, pothole, patch-pothole, manhole, and hinged-joint—were annotated. More than 60,000 images and 140,000 distresses are described in the data set. Various data augmentation methods, such as motion blur, frequency noise, and random rotation, were used to expand the original data set about 2.5 times. Resnet152 was chosen as the basic feature extraction network and pretrained with manhole images, which promoted the expression ability of the model and the convergence efficiency of training. To improve the imbalance of positive and negative samples, focal loss and mean squared error were combined as loss functions for training. In the prediction stage, modular design was adapted for each distress category for convenience of the expansion of other detection categories, enhancing the scalability of RoadID. Road markings and shadows were added to the data training as disturbances to improve the generalization performance of the model in complex scenes. The mean accuracy of the model reached 83.5%. Compared with the GODM, RoadID has advantages in both recognition accuracy and prediction speed. The proposed method can be used for rapid investigation of pavement conditions to support maintenance and repair.

Implementation of Template Matching on Detection of Stop Line Violations

Road marking is a sign located above the road surface that combines either a line or a symbol and provides information as visual guidance to road users. One example of road marking is the stop line behind the zebra crossing. Many violations of pedestrian rights are committed by motorized vehicles, especially because vehicles do not stop behind the stop line. This paper proposes a system to detect this type of traffic violation using the template matching method. This method is a technique in digital image processing to find small parts of the image that match the image template, so it can detect whether there is a traffic violation or not. This system uses the appropriate template images for each dataset (morning, afternoon, and evening). The system produces accuracy performances for the morning and afternoon dataset of about 100% and 78% respectively, and for the evening dataset is 70%. So, the overall average accuracy rate is 83%. The main factor affecting the overall accuracy performance is the inability to automatically extract the best template for the dataset from the morning and evening datasets.

DESIGN AND BUILD A PROTOTYPE UNMANNED CAR BASED ON ROAD MARKINGS WITH THE LINE DETECTION METHOD

Safety is a necessity in any case, as is driving. Many motorists today ignore safety in driving. Based on this innovation is needed in driving safety, automatic steering cars can be one of the driving safety solutions. This is because the automatic steering car uses a technological approach from various aspects ranging from deep learning, computer vision, sensor fusion, localization, control, unit path planning. In this discussion, the implementation of the line detection algorithm uses the hough transform and power steering methods. The results of the application of this method require a method to recognize the road markings with other things that are symmetrical in shape like a line, can be a shadow of a pole, a road divider, and so forth. Because the parameters of this method can only be used in ideal conditions. The overall application of this method successfully detects road markings with an accuracy of 75%

Assessment of the quality of the repair process of local roads in the rural areas of the Częstochowa and Kłobuck poviats in the context of road safety

Abstract In recent years, numerous measures to modernize local roads in rural areas have been undertaken. Unfortunately, during renovations, especially at the poviat and commune level, errors are often made as a consequence of irregularities arising at the design stage. The article presents an assessment of the quality of local road renovations carried out in the last fifteen years, in the context of maintaining operational parameters over time, using a visual method based on the term “risk”. The research was carried out for twenty-six sections of roads that were renovated or rebuilt in the Kłobuck and Częstochowa poviats. The categories of the most common errors were identified and actions were proposed to correct them and prevent new ones. Among the negative trends, the improper placement of road technical devices and the lack of appropriate marking of integral areas were distinguished. It was found that a positive aspect in the aspect of traffic safety, recently, is the widespread use of modern road marking systems and road signs, as well as greater care for proper maintenance of drainage devices. The use of modern solutions during the modernization of roads, despite co-financing from the Government Fund, is still limited by the financial deficit of local governments.

A Lane Level Bi-Directional Hybrid Path Planning Method Based on High Definition Map

A global reference path generated by a path search algorithm based on a road-level driving map cannot be directly used to complete the efficient autonomous path-following motion of autonomous vehicles due to the large computational load and insufficient path accuracy. To solve this problem, this paper proposes a lane-level bidirectional hybrid path planning method based on a high-definition map (HD map), which effectively completes the high-precision reference path planning task. First, the global driving environment information is extracted from the HD map, and the lane-level driving map is constructed. Real value mapping from the road network map to the driving cost is realized based on the road network information, road markings, and driving behavior data. Then, a hybrid path search method is carried out for the search space in a bidirectional search mode, where the stopping conditions of the search method are determined by the relaxation region in the two search processes. As the search process continues, the dimension of the relaxation region is updated to dynamically adjust the search scope to maintain the desired search efficiency and search effect. After the completion of the bidirectional search, the search results are evaluated and optimized to obtain the reference path with the optimal traffic cost. Finally, in an HD map based on a real scene, the path search performance of the proposed algorithm is compared with that of the simple bidirectional Dijkstra algorithm and the bidirectional BFS search algorithm. The results show that the proposed path search algorithm not only has a good optimization effect, but also has a high path search efficiency.

Safeguarding motorcyclists: Trialing new PRIME road markings for casualty reduction

Dedicated road markings for motorcyclists were installed on the approach to demanding bends at 10 sites across the West Highlands of Scotland. These road markings were designed as Perceptual Rider Information for Maximising Expertise and Enjoyment (PRIMEs) which presented a series of ‘gateway’ markings to encourage safer riding. Video data were collected to measure motorcycle speed, position, braking and use of the road markings, before and after the PRIMEs were installed. A total of 12,949 motorcyclists were observed. Speed reductions were observed across four of the 10 sites, changes in lateral position at the final PRIME gateway marking and apex of the bend were observed across all 10 sites and seven sites respectively; reduced braking was observed at two sites and there was an increase in the use of PRIME road markings across eight of the 10 sites. These findings are discussed in relation to the ‘Safe System’ approach to improving motorcycle safety.

High Performance Pavement Markings Enhancing Camera And LiDAR Detection

It is well known that camera and video sensors have limitations in detecting pavement markings under certain conditions e.g. glare from sunlight or other vehicles, rain, fog etc. First generations of lane keeping systems depend on visual light. Erroneous detection is also resulting from irregular road surfaces such as glossy bitumen sealing strips, rain puddles or simply worn asphalt. The role of higher performing markings and better visual camera detection has been studied with Vedecom France. LiDAR (light detection and ranging) technology could help to fill remaining gaps, as it actively sends out IR (infrared) light, that returns reliable images of the road scenario and pavement markings both day and nighttime. In order to evaluate the opportunities of LiDAR technology for the detection of road markings, 3M Company and the University of Applied Sciences in Dresden decided to work together in a joint research project. All-Weather Elements AWE, are the latest development of high-performance optics, using high index beads to provide reflectivity both in dry and wet condition. It could be determined that high performance markings help to increase the level of detection by both camera and LiDAR sensors. The AWE marking was detected from significantly longer distances, especially in wet and rainy conditions. In combination with common camera based LKA and LDW systems, the LiDAR sensors can increase the overall detection rate of pavement markings. This is especially important for vehicles with higher SAE levels of automated driving and can support the overall safety of vehicles. The research also evaluated existing test methods for wet and rain reflectivity in EN 1436 and ASTM E 2832 and how measured performance correlates with LiDAR detection.