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

An Approach for High Definition Map Information Interaction for Autonomous Driving

  • 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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