Road Markings Research Articles

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

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.

Semantic interpretation of raw survey vehicle sensory data for lane-level HD map generation

High-definition (HD) maps provide a complementary source of information for Advanced Driver Assistance Systems (ADAS), allowing them to better understand the vehicle’s surroundings and make more informed decisions. HD maps are also largely employed in virtual testing phases to evaluate the behavior of ADAS components under simulated conditions. With the advent of autonomous sensorized vehicles, raw machine-oriented data will be increasingly available. The proposed pipeline aims to provide a high-level semantic interpretation of raw vehicle sensory data to derive, in an automated fashion, lane-oriented HD maps of the environment. We first present RoadStarNet, a deep learning architecture designed to extract and classify road line markings from imagery data. We show how to obtain a semantic Bird’s-Eye View (BEV) mapping of the extracted road line markings by exploiting frame-by-frame localization information. Then, we present how to progress to a graph-based representation that allows modeling complex road line markings’ structures practically, as this representation can be leveraged to produce a Lanelet2 format HD map. Lastly, we experimentally evaluate the proposed approach in real-world scenarios in terms of accuracy and coverage performance.

Pavement crack detection through a deep-learned asymmetric encoder-decoder convolutional neural network

ABSTRACT Crack detection on roads’ surfaces is an important issue in pavement management, as it provides an indication of the quality of the road and its deterioration over time. Pavement cracks are one of the most common types of damage observed on roads, and they can be seen visually. Despite the fact that it does not provide immediate resolution to the issue, understanding the extent of crack damage is essential for the upkeep of roads. This paper presents a novel approach to automatically detecting pavement cracks using the orthoimage generated by a consumer-grade photogrammetric Unmanned Aerial Vehicle (UAV) and a deep learning algorithm. We used an autoencoder Convolutional Neural Network (CNN) to train a dataset full of challenging factors such as road lines and marks, oil and colour spots, and water stains. The model was tested on a dataset of RGB patches of different patterns of cracks and achieved an overall accuracy (OA) and F1 score of about 0.98. The results demonstrate the effectiveness of the proposed method in accurately detecting pavement cracks in challenging real-world conditions. This approach provides an efficient and cost-effective solution for pavement crack detection, that can be used for measuring the road's quality and monitoring it.

Enhancing vehicle localization by matching HD map with road marking detection

Accurate and robust vehicle localization, a fundamental task for autonomous driving, is still a challenging problem especially in GPS-denied scenarios. Recently, high definition (HD) map suggests a promising solution. However, the matching between online sensed data and HD map is difficult and time-consuming. In our work, road markings are selected as landmarks due to salient appearance features. Based on the detection results of road markings from the vehicle-borne images, the points of edge lines are employed to fit straight lines with RANSAC for outlier removal. The distances between the ego-vehicle and the fitted edge lines can be computed with camera-vehicle calibration in advance. Subsequently, the point-to-line distances from the sensed data are mapped into global linear constraints on the vehicle’s positions with the support of a lane-level HD map, which provides centimeter-level coordinates of road markings. Finally, the distances and the localization from the integrated navigation system (INS) are fused with the proposed linear Kalman filter based on the second-order Markov model (KF-MM2). The proposed method has been verified in different daily driving scenarios. Experimental results demonstrate that our method can achieve good performance with an average localization error of 0.53 m and a standard deviation of 0.17 m.

Illuminating parameters variation of horizontal road markings in the operation process

One of the most important consumer properties of a highway is traffic safety. In order to organize traffic flows and ensure safety on highways, technical means of traffic organization are used, which include horizontal road markings. The presence of easy-to-read marking schemes, especially on roads without artificial lighting, as well as in low visibility conditions, contributes to ensuring the cars safety traffic and pedestrians in accordance with the road traffic regulations. This work devoted to the dependence study of changes in the lighting parameters of horizontal road markings on technological, operational and other factors. Emphasis in the work is made on the analysis of changes in the light transmission coefficient, as this parameter is a markings visibility indicator in darkness hours. As in the darkness hours period most of the road accidents occur, which have the most severe consequences. The article analyzes foreign studies concerning the reasons for the lighting markings parameters reduction and the statistical dependencies establishment describing this process. The authors have carried out experimental studies of the road markings degradation process on public roads of II and III categories in the Moscow Region. Based on the experimental results, the influence of technological and operational factors on the road markings lighting characteristics deterioration is evaluated. The analysis was performed for different road marking lines applied by thermoplastic and cold plastic. According to the study results recommendations are given to ensure the required values of such lighting characteristics as retroreflection coefficient when applying horizontal road markings.

A Novel Way of Optimizing Headlight Distributions Based on Real Life Traffic and Eye Tracking Data Part 1: Idealized Baseline Distribution

In order to find optimized headlight distributions based on real traffic data, a three-step approach is chosen. Since the complete investigations are too extensive to fit into a single publication, this paper is the first in a series of three publications. Over three papers, a novel way to optimize automotive headlight distributions based on real-life traffic and eye-tracking data is presented, based on 119 test subjects who participated in over 15,000 km of driving, including recordings of gaze behavior, light data, detection distances, and other objects in traffic. In the present paper, a baseline headlight distribution is derived from a series of detection tests conducted under ideal conditions, with a total of three tests, each with 19–30 subjects, conducted within the same test environment. In the first test, the influence of low beam intensity on the detection of pedestrians on the sidewalk (5.0 m from the center of the driving lane) is investigated. In the second test, the influence of different high beam intensities was investigated for the same detection task. In the third test, the headlight distribution and intensity are kept constant at a representative high beam level, but the detection task is changed. In this test, the pedestrian detection target is placed along different detection angles, ranging from immediately adjacent to the road (2.5°) to 15.5 m away from the center of the driving lane (8.0°). As mentioned, all of these tests were conducted under ideal conditions, with the studies taking place on an airfield with a 1.2 km long straight road and normal road markings, but without oncoming traffic, tasks other than keeping the vehicle with cruise control within its lane, or other distracting objects present. The tests yielded two sets of data; the first is the intensity, based on the first two studies, needed to ensure sufficient intensity to detect objects under ideal conditions at distances needed for different driving speeds. The last test then uses these intensities and necessary variations in the required intensity to create an idealized, symmetric headlight distribution as a baseline for subsequent publications. Although the distribution applies only to passenger vehicles like the one used in the test, the same approach could be applied to other vehicle types. The second paper of this series will focus on real traffic objects and their distributions within the traffic space in order to identify relevant areas in headlight distribution when driving under real traffic conditions. The third paper of this series will analyze driver gaze distributions during real driving scenarios. The data from all three papers are used to create optimized headlight distributions, thereby showing how such an optimized distribution relates to current headlight distributions in terms of luminous flux, intensity, and overall distribution.

Automated Segmentation and Deterioration Determination of Road Markings

Automated Segmentation and Deterioration Determination of Road Markings

Designing Machine Learning Algorithm for Autonomous Driving in the Snow

Autonomous vehicles have evolved to make decisions with great confidence and accuracy. Systems like the Waymo self-driving clearly have the ability to control a vehicle with level 4 autonomy under good conditions where lane markings and other driving guides like road signs are clearly visible [2]. Elon Musk has announced that Tesla autopilot should reach level 4 in 2023 [3]. However, many such systems fail to perform at a similar level in non-perfect weather conditions. The most difficult such weather appears to be a snowy climate as the vehicle significantly underperforms and the autonomous functionality is often not applicable [1]. This paper aims to train a model capable of navigating snowy weather by collecting data pertaining specifically to this weather, including unorthodox visual cues to stand in place of road markings. Such cues include tire tread marks and surrounding snow piles. These serve as a viable substitute for lane markings, allowing the vehicle to operate safely while mimicking human driving tendencies. Such a model is able to effectively drive in both clear and snowy weather when tested in simulation software. We consider a model effective for the purposes of this study in the snow when it is able to execute a full turn before failure.

Evaluasi Rambu dan Marka Pada Kota Serang Ruas Jalan Veteran - Jalan Jendral Ahmad Yani

One of the vital key collector roads in Serang City is Jl. Veteran - Jl. General Ahmad. This road serves an important purpose because it is one of the key routes in Banten Province. However, traffic congestion and accidents are common on this road. The purpose of this study was to determine the number of traffic signs and road markings on Jl. Veteran - Jl. General Ahmad Yani and whether they are in accordance with PERMENHUB No. 13 of 2014 and No. 67 of 2018, as well as to determine the location of the Directional Signs (RPPJ) and whether they are in accordance with the stopping sight distance analysis (JPH) according to PERMENHUB No. 13 of 2014. The analysis of the suitability of signs from 78 signs and 24 road markings on Jl. Veteran - Jl. General Ahmad Yani revealed that 23 signs (29.49%) did not comply with PERMENHUB regulations No.13 of 2014, and 11 road markings (45.83%) did not comply with PERMENHUB regulations No.67 of 2018. The result of this study also revealed that 3 signs (50.00%) of the 6 observed signs fulfilled the stopping sight distance analysis on Jl. Veteran - Jl. General Ahmad Yani and the reverse direction.

Areas of intervention for enhancing the knowledge of safe driving: An experience in West Bengal, India

Road safety is a global concern, and India is considered to be the most unsafe for road users, accounting for almost 11% of the accident-related deaths. With 80% of road accidents occurring due to driving error, lack of safe driving knowledge was found to be the root cause of accidents in India. This paper reports an investigation for identifying areas of interventions for enhancing the safe driving knowledge of drivers. Responses were collected from trainers of motor training schools as they have vast experience of driving in Indian road and traffic environment, understand the requirements of knowledge for safe driving and are also aware of the present level of emphasis on different aspects of safe driving. Responses from over 250 trainers of Motor Training Schools in West Bengal, India, were recorded. The collected data includes the importance of various aspects of safe driving knowledge and knowledge adequacy of the drivers in the present scenario on Likert-type scales. The safe driving aspects considered in the study are roadway elements, road signs and markings, general and vehicle-specific safe driving rules, complex and sensitive environment, vulnerable road users, attitude and alertness, accident and emergencies, first aid, and legal aspects. The study employs Revised Importance Performance Analysis with fuzzy c-means algorithm to identify intervention areas based on trainers’ perception. The safe driving aspects were classified under two management schemes, namely ‘Concentrate Here’ and ‘Next Priority’. Knowledge of Alertness and Attitude, Road Rules, Road Signs, and Road Markings were identified as major areas of intervention.

Detection of Optical Road Markings With Automotive Radar Sensors

Detection of Optical Road Markings With Automotive Radar Sensors

Study of traffic-related pollution and its treatment with a particular focus on microplastics in tunnel wash and road runoff water.

Treatment of tunnel wash runoff water and road runoff water before it reaches the environment is recommended to limit the negative consequences of traffic-related pollution. The efficiency of existing water treatment systems to remove traffic-related microplastic (MP) has not been sufficiently documented. Expanding the knowledge about traffic-related MP and documenting the treatment efficiency of MP in road tunnel wash water (TWW) and road runoff (RRW) treatment systems were the objectives of the presented project. TWW from the Tåsen tunnel, Norway, and RRW from the Fossbekken sedimentation pond were investigated in summer and winter situations. Six commonly available polymer types, tire rubber tread particles (TRP), and road marking paints (RMP) were analyzed in the selected samples. About 0.12 and 0.26 μg/L of polymers were identified in winter and summer TWWs. Significantly higher tire rubber and road marking paint concentrations were identified in the winter sample compared to summer sample. Suspended particle concentration in the Fossbekken RRW treatment pond effluent was lower in the summer than in the winter sample. About 0.002 and 0.0008 μg/L polymer masses were identified in winter and summer samples, respectively. TRP in the winter and summer samples were 0.7 and 0.2 μg7/L, and 13.4 μg/L RMP was found in the winter sample, while it was only 0.008 μg/L in the summer sample.

Planning for walkable cities in Africa: Co-producing knowledge on conditions, practices, and strategies

This study sought to unravel the conditions of the walking environment, and residents lived experiences of walking in two urban neighbourhoods in the Greater Accra Metropolitan Area (GAMA). Drawing on a participatory mapping exercise and a total of 70 community and institutional qualitative interviews, the results revealed that the study neighbourhoods have precarious walking conditions manifested by the absence of road markings, inadequate traffic lights and other road-calming infrastructure such as speed ramps. The results also show that previous and current national and local development plans do little to plan for the walking environment. This study demonstrates the impacts of the failure of planning and policy responses to the walking environment on urban residents. In an important stakeholder engagement workshop involving a total of 40 participants, our study brought academics, institutional representatives and residents from the two neighbourhoods together to discuss the findings and establish a stronger collaborative relationship for designing an equitable and sustainable walking environment. Based on the findings from the qualitative interviews and stakeholder engagement workshop, we therefore recommend a community-based participatory planning strategy for improving walkability conditions.

EFEKTIVITAS PENGGUNAAN JALUR LAMBAT PADA RUAS JALAN DR. LUKMONOHADI DAN DR. RAMELAN KUDUS DI KABUPATEN KUDUS

Kudus street is a national road that connects Jakarta-Semarang-Surabaya-Banyuwangi, on this north coast road as the need for mobility of goods delivery or mobility of residents between cities. On the way Dr. Lukmonohadi – Dr. Ramelan has a slow lane on the left side of the road. The purpose of the existence of the slow lane on road DR. Lukmonohadi-Dr. Ramelan is to smooth traffic and reduce the fatality rate of accidents in the fast lane, but due to very high shopping and industrial activities that can interfere with performance on these roads. The purpose of this research study is to review the effectiveness of the slow lane on the Dr. Lukmonohadi-Dr. The Ramelan is working properly or not. From the results of the analysis of traffic performance measured using the degree of saturation (DS) for Road Dr. Lukmonohadi of 0.57, while Road Dr. Ramelan is 0.61. From the level of congestion that can be obtained from the value of the side barriers that Dr. Lukmonohadi until Dr. Ramelan is included in the medium side resistance class (M). Based on the comparison of the accident rate from 2016-2021 the number of accidents continues to increase. The recommended recommendations are that it is necessary to repair and add road facilities such as repair of separator paint, faded road markings, addition of zebra crossings in pedestrian-congested locations and asphalt repairs on slow lanes because there is a lot of asphalt that is peeling and has holes, it is very dangerous for road users crossing it. Based on the results of the analysis for the slow lane, it can be concluded that the slow lane has been effective in facilitating traffic flow to reduce the number of accidents on the fast lane.

Traffic planning for the protection of vulnerable groups of persons exemplified by the City of Munich

Pedestrian and bicycle traffic make asignificant contribution to the traffic turnaround. Measures to improve the road safety of pedestrians and cyclists are insofar an important building block to asuccessful sustainable city and traffic planning. The mobility strategy 2035 of the City of Munich includes partial strategies for walking and cycling as well as for road safety, which was confirmed through former city council resolutions on Vision Zero. The most important measures for the comfort and safety of pedestrians are aspeed limit of 30 km/h, wide and free sidewalks (without obstacles) and crossing assistance with good visibility conditions. Depending on the local situation, sidewalk extensions, road islands, pedestrian crossings (zebra crossings) and traffic lights with attractive circuit conditions for pedestrians make crossing easier. The comfort and safety of cyclists can be improved with broad cycling paths along the main streets. They should allow the overtaking of cyclists in both directions. At least on side streets, acomprehensive speed limit of 30 km/h is an important issue. One-way streets should be open for oncoming cyclists against the one-way direction. At road crossings and road junctions the visibility of cyclists should be improved with road markings and widened bike lanes and there should be aconflict-free circuit of traffic lights, especially where there is ahigh percentage of commercial vehicles.

Sequential Esterification-Diels-Alder Reactions for Improving Pine Rosin Durability within Road Marking Paint.

Pine rosin, which is derived from Pinus merkusii resin, a natural product, has demonstrated potential as a road marking binder. Although pine rosin has an excellent shinning property, it has some limitations, such as instability and color change. To tackle these issues, modified rosin has been developed through sequential esterification and Diels-Alder reactions, and it has shown better properties than untreated rosin. In this study, from the evaluation of untreated and treated rosins, the treated rosin showed some improvements, such as a lower acid value and higher stability, as shown by the color consistency during the oxidation test at 150 °C for 24 h in open-air conditions. Additionally, as road marking paint, the modified rosin is blended with blending materials in the range of 18-28 wt.%. The modified rosin has a softening point of 170-210 °C, a hardness of 50-71 HD, and a weight loss of 1.33-5.12 mg during the wearing test. These results are comparable to or better than those of commercially available road marking products.

Skid resistance of road markings: literature review and field test results

Skid resistance of road markings belongs to their important performance parameters, significantly influencing the safety of all road users, particularly when the roadway is wet – yet it is very seldom addressed. A review of the sparse literature on this topic is provided herein. To demonstrate the effect of the selection of anti-skid particles on Pendulum Test Value (PTV) of road markings, a field experiment was done with five drop-on materials. Whereas PTV of the asphalt road surface was 49, covering it with a paint without any glass beads or anti-skid particles caused PTV decrease to 35; utilisation of glass beads led to an increase in PTV to 45 and the use of a mixture of glass beads with 10% of corundum provided PTV 50. The achieved initial PTV and its retention upon the usage of road markings depended on the selection of the anti-skid particles. The initial PTV were in the range of 45–65, after 10 months they decreased to 41-49 (i.e. by 6-32%). The results confirm that not all anti-skid particles are equal and that the initial PTV cannot be used to reliably predict long-term performance.

Kebiasaan Melanggar Peraturan Pengguna Jalan Sebagai Objek Penciptaan Karya Seni Lukis

Abstract The habit of violating the rules by road users became a source of ideas in creating this work of art. The asphalt road and the icons on the road are used as objects for creating works. Starting from observing the behavior and habits of road users and public facilities who do not comply with applicable regulations, violate traffic signs, disobey road markings and so on. The author uses forms or icons that are often found on the streets, such as; traffic signs, marking lines, road asphalt, car tire tracks and so on onto the canvas using a realist-symbolic style. The realist-symbolic concept is used to convey messages to people who see or enjoy this work. The method used in the process of making this work goes through several stages, namely; (1) observation stage (2) contemplation stage (3) sketch stage (4) embodiment stage. Keywords: the habit of breaking the rules, streets, painting

ASA-BiSeNet: improved real-time approach for road lane semantic segmentation of low-light autonomous driving road scenes.

The solution to the problem of road environmental perception is one of the essential prerequisites to realizing the autonomous driving of intelligent vehicles, and road lane detection plays a crucial role in road environmental perception. However, road lane detection in complex road scenes is challenging due to poor illumination conditions, the occlusion of other objects, and the influence of unrelated road markings. It also hinders the commercial application of autonomous driving technology in various road scenes. In order to minimize the impact of illumination factors on road lane detection tasks, researchers use deep learning (DL) technology to enhance low-light images. In this study, road lane detection is regarded as an image segmentation problem, and road lane detection is studied based on the DL approach to meet the challenge of rapid environmental changes during driving. First, the Zero-DCE++ approach is used to enhance the video frame of the road scene under low-light conditions. Then, based on the bilateral segmentation network (BiSeNet) approach, the approach of associate self-attention with BiSeNet (ASA-BiSeNet) integrating two attention mechanisms is designed to improve the road lane detection ability. Finally, the ASA-BiSeNet approach is trained based on the self-made road lane dataset for the road lane detection task. At the same time, the approach based on the BiSeNet approach is compared with the ASA-BiSeNet approach. The experimental results show that the frames per second (FPS) of the ASA-BiSeNet approach is about 152.5 FPS, and its mean intersection over union is 71.39%, which can meet the requirements of real-time autonomous driving.

Study on the Reflective Principle and Long-Term Skid Resistance of a Sustainable Hydrophobic Hot-Melt Marking Paint

Road marking is very important for driving safety and reducing the accident rate as a basic component of highway construction. However, traditional road marking paints are prone to be worn after short-term application and have poor durability and reflective performance. To address these problems, the marking paint was modified using the organic polymer material polytetrafluoroethylene to create a durable hydrophobic hot-melt marking paint. The factors affecting the reflective performance of marking lines are analyzed, and artificial accelerated abrasion tests were carried out to analyze the skid resistance and marking retroreflection coefficient of hydrophobic coatings. Results show that the texture of the glass beads and the quality of the coating plays a major role in the reflective performance of the marking line. The friction coefficient value of the modified marking paint is 4.62% higher than that of the traditional hot-melt marking paint. The retroreflection performance of the marking paint with 4% hydrophobic material is 8.45% higher than the initial value of the retroreflection coefficient of the traditional hot-melt marking paints. This sustainable hydrophobic hot-melt marking paint is safer and more durable than traditional pavement marking paints, which may save follow-up maintenance resources and cost from the sustainable aspect.