• Title/Summary/Keyword: Local dynamic map(LDM)

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A Study on Dynamic Map Data Provision System for Automated Vehicle (자율주행을 위한 동적지도정보 제공에 관한 연구)

  • Yang, Inchul;Jeon, Woo Hoon;Lee, Hyang Mi
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.16 no.6
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    • pp.208-218
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    • 2017
  • This study aims to develop the Vehicle Local Dynamic Map (V-LDM) and demonstrate its performance for providing dynamic map data efficiently to the vehicle control module. Firstly, the concept of the in-vehicle LDM has been established and then the system has been carefully designed according to the international standards. The high-precision digital map embedded in LDM has been designed to incorporate the lane-level information of road network, and the Dynamic Map protocol (DM protocol) which is a message protocol including the road data with dynamic traffic event data has been defined. The performance test of the proposed system has been conducted in the uninterrupted road section of Kyungbu expressway, showing that both of the data size and the elapsed time to finish the process are almost linearly proportional to the length of target road. Finally, it is recommended that the length of target road for DM protocol be less than 250m.

A Framework for Calculating the Spatiotemporal Activation Section of LDM-Based Autonomous Driving Information (동적지도정보 기반 자율주행 정보의 시공간적 활성화 구간 산정 프레임워크)

  • Kang, Chanmo;Chung, Younshik;Park, Jaehyung
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.42 no.4
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    • pp.519-526
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    • 2022
  • Basically, autonomous vehicles drive using road and traffic information collected by various sensors. However, it is known that there is a limitation to realizing fully autonomous driving with only such technologies and information. In recent, various efforts are being made to overcome the limitations of sensor-based autonomous driving, and efforts are also underway to utilize more specific and accurate road and traffic information, called local dynamic map (LDM). However, LDM-related data standards and specifications have not yet been sufficiently verified, and research on the spatiotemporal scope of LDM during autonomous driving is extremely limited. Based on this background, the purpose of this study is to identify these limitations through an analysis of previous LDM-related studies and to present a framework for calculating the spatiotemporal activation section of LDM-based road and traffic information.

Evaluation of LDM (Local Dynamic Map) Service Based on a Role in Cooperative Autonomous Driving with a Road (자율협력주행을 위한 역할 기반 동적정보 서비스 평가 방법)

  • Roh, Chang-Gyun;Kim, Hyoungsoo;Im, I-Jeong
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.21 no.1
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    • pp.258-272
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    • 2022
  • The technology implementation method was diversified into an 'autonomous cooperative driving' method to overcome the limitations of a stand-alone autonomous vehicle with vehicle sensor-based autonomous driving. The autonomous cooperative driving method involves exchanging information between roadside infrastructure and autonomous vehicles. In this process, the concept of dynamic information (LDM), a target of cooperation, was established. But, evaluation methods and standards for dynamic information have not been established. Therefore, this study, a dynamic information evaluation method based on information on pedestrians within the moving objects. In addition, autonomous cooperative driving was demonstrated, and dynamic information was also verified through the evaluation method. The significance of this study is that it established the dynamic information evaluation methodology for autonomous cooperative driving for the first time. Based on this, this study is expected to contribute to the application of safe autonomous cooperative driving technology to the field.

Evaluation of Road and Traffic Information Use Efficiency on Changes in LDM-based Electronic Horizon through Microscopic Simulation Model (미시적 교통 시뮬레이션을 활용한 LDM 기반 도로·교통정보 활성화 구간 변화에 따른 정보 이용 효율성 평가)

  • Kim, Hoe Kyoung;Chung, Younshik;Park, Jaehyung
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.43 no.2
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    • pp.231-238
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    • 2023
  • Since there is a limit to the physically visible horizon that sensors for autonomous driving can perceive, complementary utilization of digital map data such as a Local Dynamic Map (LDM) along the probable route of an Autonomous Vehicle (AV) is proposed for safe and efficient driving. Although the amount of digital map data may be insignificant compared to the amount of information collected from the sensors of an AV, efficient management of map data is inevitable for the efficient information processing of AVs. The objective of this study is to analyze the efficiency of information use and information processing time of AV according to the expansion of the active section of LDM-based static road and traffic information. To carry out this objective, a microscopic simulator model, VISSIM and VISSIM COM, was employed, and an area of about 9 km × 13 km was selected in the Busan Metropolitan Area, which includes heterogeneous traffic flows (i.e., uninterrupted and interrupted flows) as well as various road geometries. In addition, the LDM information used in AVs refers to the real high-definition map (HDM) built on the basis of ISO 22726-1. As a result of the analysis, as the electronic horizon area increases, while short links are intensively recognized on interrupted urban roads and the sum of link lengths increases as well, the number of recognized links is relatively small on uninterrupted traffic road but the sum of link lengths is large due to a small number of long links. Therefore, this study showed that an efficient range of electronic horizon for HDM data collection, processing, and management are set as 600 m on interrupted urban roads considering the 12 links corresponding to three downstream intersections and 700 m on uninterrupted traffic road associated with the 10 km sum of link lengths, respectively.

항만용 자율협력주행 동적지도(LDM) 및 관제용 위치인식 설계 방안 연구

  • Kim, Gil-Tae
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2020.11a
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    • pp.157-158
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    • 2020
  • 항만용 자율주행 야드트럭운행 환경은 무신호교차로 주행, 낮은 GPS정확도, 악천후상황주행, 이송 컨테이너 위치변경등과 같이 일반 도로의 센서기반의 자율주행차량 운행과 다르게 매우 복잡하다. 이를 위해서는 항만내 특성을 반영한 실시간 위치, 속도 등에 대한 정확한 인식이 중요한 요소이다. 이를 위해서 센서융합과 V2X기반의 복합적인 항만용 실시간 로컬 동적지도 (Local Dynamic Map) 생성 및 V2X기반의 협력측위를 통하여서 기존의 독립적인 자율주행차량의 위치 인식보다 더 개선된 고정밀 위치 인식 정보추출이 필요하다. 본 연구에서는 복합적인 항만용 동적지도 생성관리시스템의 설계 방안 및 협력측위 기술 적용 방안을 제시하고 이를 활용한 항만 구역내 자율주행차량 및 모든 화물 이송장비들의 실시간 위치 인식뿐만 아니라 이동체의 사전 충돌예측 및 비상정지 안전 제어 가능한 V2X 기반의 인텔리젼스 한 3차원 관제시스템 설계 방안을 제시하고자 한다.

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A study on the Evaluation of Real-Time Map Update Technology for Automated Driving (자율주행 지원을 위한 정밀도로지도 갱신기술 평가를 위한 기준 도출 연구)

  • PARK, Yu-Kyung;KANG, Won-Pyung;CHOI, Ji-Eun;KIM, Byung-Ju
    • Journal of the Korean Association of Geographic Information Studies
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    • v.22 no.3
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    • pp.146-154
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    • 2019
  • Recently, a system has been developed and applied to establish and utilize HD maps through R&D. The biggest problem, however, is the lack of a proper HD map update system, which requires the development and adoption of such a system as soon as possible. In addition, in the case of updating HD maps for automated driving, integrity and accuracy of maps are required for safe driving, so an test of these technologies and data quality is required. In April 2018, the Ministry of Land, Infrastructure and Transport implemented a project to 'Develop Technology to Demonstrate and Share the Instant Road Change Detection and Update Technology for automated driving. This paper analyzed the technology for updating map based on the investigation and analysis of relevant technology trends for the development of integrated demonstration and sharing technology of road change rapid detection and updating map technology, and put forward the criteria for road change rapid detection, integrated quality verification of update technology. It is expected that the results of this study will contribute to quality assurance of HD maps that support safety driving for automated vehicles.