张焱杰, 黄炜, 刘信陶, 张丰源, 吴杭彬, 应申, 刘春. 自动驾驶高精地图信息交互方法[J]. 武汉大学学报 ( 信息科学版), 2024, 49(4): 662-671. DOI: 10.13203/j.whugis20230166
引用本文: 张焱杰, 黄炜, 刘信陶, 张丰源, 吴杭彬, 应申, 刘春. 自动驾驶高精地图信息交互方法[J]. 武汉大学学报 ( 信息科学版), 2024, 49(4): 662-671. DOI: 10.13203/j.whugis20230166
ZHANG Yanjie, HUANG Wei, LIU Xintao, ZHANG Fengyuan, WU Hangbin, YING Shen, LIU Chun. An Approach for High Definition Map Information Interaction for Autonomous Driving[J]. Geomatics and Information Science of Wuhan University, 2024, 49(4): 662-671. DOI: 10.13203/j.whugis20230166
Citation: ZHANG Yanjie, HUANG Wei, LIU Xintao, ZHANG Fengyuan, WU Hangbin, YING Shen, LIU Chun. An Approach for High Definition Map Information Interaction for Autonomous Driving[J]. Geomatics and Information Science of Wuhan University, 2024, 49(4): 662-671. DOI: 10.13203/j.whugis20230166

自动驾驶高精地图信息交互方法

An Approach for High Definition Map Information Interaction for Autonomous Driving

  • 摘要: 高精地图在自动驾驶应用落地的进程中发挥着重要作用,目前大多数研究着眼于高精地图中道路几何结构的高精度表征和道路静态信息的更新。然而,对路网交通中存在的大量且具有时效性的动态信息的相关研究尚有欠缺。从动态信息的内容、数据结构、信息交互3个方面展开,研究高精地图动态信息。对国内外在高精地图数据组织结构和数据标准方面的工作进行整理和分析,提出了一套完整的高精地图动态信息所需涵盖的内容。将自动驾驶车辆车端和高精地图信息系统云端作为信息交互的两大主体,提出在车端、云端不同的组合模式下信息交互的方法,实现高精地图动态信息内容交互。提出的信息交互方法在局部的自动驾驶车辆与高精地图信息系统之间的交互过程中,逐步聚合到最终能够打通全域的高精地图信息数据流,实现高精地图动态信息数据的汇聚和整合,更好地服务和助力自动驾驶汽车在复杂道路环境下的导航决策能力。

     

    Abstract:
    Objectives High definition(HD) map plays an important role in autonomous driving. Most existing research focuses on high-resolution mapping in terms of road geometries as well as the updating of static road information in HD map. However, there is still a lack of research on dynamic information involved in road traffic networks. We focus on enriching the content of HD map in terms of the dynamic information including the dynamic information, it's meta data structure and inforamation interaction.
    Methods We propose a comprehensive set of content that HD map need to cover for the dynamic information based on existing work on HD map data organization and data standards. Based on this, combined with cloud ends of HD map systems and vehicle ends of autonomous driving vehicles as two information interaction terminals, a method for information interaction between cloud ends and vehicle ends under different combination modes (vehicle-cloud, vehicle-vehicle, and cloud-vehicle modes) used in HD map information system is proposed to facilitate a timely capture of dynamic information in road environments.
    Results (1) The vehicle-cloud information interaction mode is suitable for self-driving cars. The vehicle collects vehicle dynamic information in real time and uploads it to the cloud of the HD map information system after preprocessing, so as to realize the sharing of perception information of different vehicles in the road environment on the cloud. (2)The vehicle-to-vehicle information interaction mode is used between different self-driving cars. This mode is mainly aimed at obtaining partially vehicle dynamic information directly through vehicle-to-vehicle interaction during driving. (3)The cloud-vehicle information interaction mode is suitable for vehicles connected to the HD map information system platform. The cloud stores dynamic information, and the vehicle requests the demanding information according to its own needs. Therefore, The HD map information system continuously performs information interaction in three interaction modes, and updates information simultaneously, maintaining the freshness of dynamic information and enhancing the robustness of HD map.
    Conclusions However, due to the complexity and variability of the road environment, the content of dynamic information interaction we propose can only cover what happens during vehicle driving to a certain extent. For the road information that appears with a small probability,we do not conduct in-depth research, which is also the direction we need to discuss and study in the future.

     

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