• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.39-51
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• 2021

Recently, there has been much interest in practical use of standardized HD map that can effectively define roads, lanes, junctions, road signs, and road facilities in autonomous driving. Various kinds of de jure or de facto standards such as ISO 22726-1, ISO 14296, HERE HD Live map, NDS open lane model, OpenDRIVE, and NGII HD map are currently being used. However, there are lots of differences in data modeling among these standards, it makes difficult to use them together in autonomous driving. Therefore, we propose a data model conversion method to enable an efficient use of various kinds of HD map standards in autonomous driving in this study. Specifically, we propose a conversion method between the NGII HD map model, which is easily accessible in the country, and the OpenDRIVE model, which is commonly used in the autonomous driving industry. The proposed method consists of simple conversion of NGII HD map layers into OpenDRIVE objects, new OpenDRIVE objects creation corresponding to NGII HD map layers, and linear transformation of NGII HD map layers for OpenDRIVE objects creation. Finally, we converted some test data of NGII HD map into OpenDRIVE objects, and checked the conversion results through Carla simulator. We expect that the proposed method will greatly contribute to improving the use of NGII HD map in autonomous driving.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.132-145
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• 2020

By coming of the 4th industrial revolution era, HD map have became a key infrastructure for determining precise location of autonomous driving in areas of futuristic cars, logistics and robots. Autonomous vehicles have became more dependent on HD map to determine the exact location of objects detected by various sensors such as LiDAR, GNSS, Radar, and stereo cameras as well as self-location decisions. By actualizing autonomous driving and C-ITS technologies, the demand for precise information on HD map have increased. And also the demand for the creation of new information based on the convergence of various changes and real-time information have increased. In this study, domestic and international HD map standards and related environments have analyzed. Based on this, usability has researched which comparison with standard HD map established by various institutions. Additionally, usability of standard HD map have studied for applying actual autonomous vehicles by reworking HD map. By the result of study, standard HD map have well established to use by various institutions. If further research about layer classification and definition by institutions will carried out based on this study, it has expected that and efficient establishment and renewal of HD map will take place.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.3
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• pp.120-132
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• 2019

As a key infrastructure for positioning autonomous vehicles, HD maps are making efforts to equip autonomous vehicles and connect them with services worldwide. Global companies like Here and TomTom have contracted an agreement with three German automobile companies(BMW, Audi, Daimler) by establishing systems including from the production of HD maps to loading autonomous vehicles. Japan has organized a DMP(Dynamic Map Platform) by collaborating public and private section and performing establishment of HD map on private section-oriented. Korean companies related with autonomous vehicles are also cooperating with various companies such as vehicles, sensors and maps. But public and private sections are establishing HD maps respectively. Accordingly, Ministry of Land, Infrastructure and Transport implemented and signed memorandum for private and public cooperation in April 2019, Joint establishment council of HD map has launched in August 2019. This study performed domestic and foreign trend analysis about HD map and interviewed to related companies. And analyzed solutions for interactive cooperation of private and public section in HD map industry, purposed step-by-step strategy for operation and operation plan for joint establishment council of HD map.

• Journal of Auto-vehicle Safety Association
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• v.15 no.2
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• pp.42-48
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• 2023

In recent years, automated vehicles have garnered attention in the multidisciplinary research field, promising increased safety on the road and new opportunities for passengers. High-Definition (HD) maps have been in development for many years as they offer roadmaps with inch-perfect accuracy and high environmental fidelity, containing precise information about pedestrian crossings, traffic lights/signs, barriers, and more. Demonstrating autonomous driving requires verification of driving on actual roads, but this can be challenging, time-consuming, and costly. To overcome these obstacles, creating HD maps of real roads in a simulation and conducting virtual driving has become an alternative solution. However, existing HD maps using high-precision data are expensive and time-consuming to build, which limits their verification in various environments and on different roads. Thus, it is challenging to demonstrate autonomous driving on anything other than extremely limited roads and environments. In this paper, we propose a new and simple method for implementing HD maps that are more accessible for autonomous driving demonstrations. Our HD map combines the CARLA simulator and OpenStreetMap (OSM) data, which are both open-source, allowing for the creation of HD maps containing high-accuracy road information globally with minimal dependence. Our results show that our easily accessible HD map has an accuracy of 98.28% for longitudinal length on straight roads and 98.42% on curved roads. Moreover, the accuracy for the lateral direction for the road width represented 100% compared to the manual method reflected with the exact road data. The proposed method can contribute to the advancement of autonomous driving and enable its demonstration in diverse environments and on various roads.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.115-121
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• 2024

Each ADS must have a validation and evaluation scenario for ODD. This requires a large number of scenarios, so a scenario library must be developed. In order to effectively utilize the scenario library, a system that supports testing in the ODD of the user's choice is required. In other words, in order to develop a scenario library, it is necessary to build a database on actual driving road conditions (geometry, etc.). Accordingly, in this study, we establish a domestic driving environment database based on HD-Map for driving safety testing, design a system that can search test target sections in connection with the ODD of the scenario, and present the implementation results. In the future, it is expected that the domestic driving environment database will be able to create scenarios through linking with the scenario library and directly utilize them for scenario-based evaluation of various demand sources.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.1
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• pp.273-286
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• 2022

An HD map is essential in the automated driving of level 4 and above to generate the vehicle's global path since it contains road information and each road's lane information. Therefore, all the road elements in the HD map must be correctly defined to construct the correct road network necessary to generate the global path. But unfortunately, it is not difficult to find various errors even in the most recent HD maps. Hence, a preprocessing algorithm has been developed to detect and correct errors in the HD map. This error detection and correction result in constructing the correct road network for use in global path planning. Furthermore, the algorithm was tested on real roads' HD maps, demonstrating its validity.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1163-1175
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• 2021

In autonomous driving, road markings are an essential element for object tracking, path planning and they are able to provide important information for localization. This paper presents an approach to update and measure road surface markers with HD maps as well as matching using inverse perspective mapping. The IPM removes perspective effects from the vehicle's front camera image and remaps them to the 2D domain to create a bird-view region to fit with HD map regions. In addition, letters and arrows such as stop lines, crosswalks, dotted lines, and straight lines are recognized and compared to objects on the HD map to determine whether they are updated. The localization of a newly installed object can be obtained by referring to the measurement value of the surrounding object on the HD map. Therefore, we are able to obtain high accuracy update results with very low computational costs and low-cost cameras and GNSS/INS sensors alone.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.112-121
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• 2021

Unlike a typical small-sized robot navigating in a free space, an autonomous vehicle has to travel in a designated road which has lanes to follow and traffic rules to obey. High-Definition (HD) maps, which include road markings, traffic signs, and traffic lights with high location accuracy, can help an autonomous vehicle avoid the need to detect such challenging road surroundings. With space constraints and a pre-built HD map, a new type of path planning algorithm can be conceived as a substitute for conventional grid-based path planning algorithms, which require substantial planning time to cover large-scale free space. In this paper, we propose an obstacle-avoiding, cost-based planning algorithm in a continuous space that aims to pursue a globally-planned path with the help of HD map information. Experimentally, the proposed algorithm is shown to outperform other state-of-the-art path planning algorithms in terms of computation complexity in a typical urban road setting, thereby achieving real-time performance and safe avoidance of obstacles.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.4
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• pp.375-382
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• 2020

As the 4th Industrial Revolution era in worldwide, interest in autonomous vehicles is increasing. but due to recent safety issues such as pedestrian accidents and car accidents, as a technical model for this, the demand for 3D HD maps (High Definition maps) is increasing in including lanes, road markings, road information, traffic lights and traffic signs etc. However, since some complementary points have been continuously raised according to demand, It is necessary to collect the opinions of institutions and companies utilizing HD maps and to improve HD maps. This study was conducted by utilizing the results of the contest for usability verification of HD Maps hosted by the National Geographic Information Institute and organized by the Spatial Information Industry Promotion Institute. For this study, we researched HD maps' layers and codes for HD maps object usability to improve HD maps, constructed HD maps object usability items accordingly, and contested usability verification of HD maps according to the items The contestants conducted verification and analyzed the results. As a result, the most frequently used code for each layer was the flat intersection, and the code showing the highest usage rate was a safety sign. In addition, the use rate of the sub-section and height obstacles was 16.67% and 8.88%, respectively, showing a low ratio. In order to utilize HD maps in the future, this study is expected to require research to continuously collect opinions from customers and improve data objects and data models that are actually needed by customers.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.186-201
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• 2021

As autonomous driving technology advances, the accuracy of the vehicle position is important for recognizing the environments around driving. Map-matching localization techniques based on high definition (HD) maps have been studied to improve localization accuracy. Because conventional map-matching techniques estimate the vehicle position based on an HD map reference dataset representing the center of the lane, the estimated position does not reflect the deviation of the lateral distance within the lane. Therefore, this paper proposes a localization system based on the reference lateral position dataset extracted using image processing and HD maps. Image processing extracts the driving lane number using inverse perspective mapping, multi-lane detection, and yellow central lane detection. The lane departure method estimates the lateral distance within the lane. To collect the lateral position reference dataset, this approach involves two processes: (i) the link and lane node is extracted based on the lane number obtained from image processing and position from GNSS/INS, and (ii) the lateral position is matched with the extracted link and lane node. Finally, the vehicle position is estimated by matching the GNSS/INS local trajectory and the reference lateral position dataset. The performance of the proposed method was evaluated by experiments carried out on a highway environment. It was confirmed that the proposed method improves accuracy by about 1.0m compared to GNSS / INS, and improves accuracy by about 0.04m~0.21m (7~30%) for each section when compared with the existing lane-level map matching method.