结合行驶场景语义的轨迹-路网实时匹配方法

doi:10.11947/j.AGCS.2021.20210261

测绘学报 ›› 2021, Vol. 50 ›› Issue (11): 1617-1627.doi: 10.11947/j.AGCS.2021.20210261

结合行驶场景语义的轨迹-路网实时匹配方法

傅琛^1,2, 黄升钶¹, 汤焱¹, 吴杭彬^1,3, 刘春^1,3, 姚连璧^1,3, 黄炜¹

1. 同济大学测绘与地理信息学院, 上海 200092;
2. 北京大学地球与空间科学学院, 北京 100871;
3. 同济大学城市交通研究院, 上海 200092

收稿日期:2021-05-11 修回日期:2021-10-27 发布日期:2021-12-07
通讯作者: 吴杭彬 E-mail:hb@tongji.edu.cn
作者简介:傅琛(1999—),女,硕士生,研究方向为轨迹数据挖掘。
基金资助:
国家重点研发计划（2018YFB1305003）；国家自然科学基金（41771482；41771481；42171452）

A real-time map matching method for road network using driving scenario classification

FU Chen^1,2, HUANG Shengke¹, TANG Yan¹, WU Hangbin^1,3, LIU Chun^1,3, YAO Lianbi^1,3, HUANG Wei¹

1. College of Surveying and Geo-Informatics, Tongji University, Shanghai 200092, China;
2. School of Earth and Space Sciences, Peking University, Beijing 100871, China;
3. Urban Mobility Institute, Tongji University, Shanghai 200092, China

Received:2021-05-11 Revised:2021-10-27 Published:2021-12-07
Supported by:
The National Key Research and Development Program of China (No. 2018YFB1305003);The National Natural Science Foundation of China (Nos. 41771482;41771481;42171452)

摘要/Abstract

摘要： 实时地图匹配技术在智能交通、自动驾驶等领域起着关键作用。现有实时地图匹配算法在高架、立交道路等复杂场景受到平行道路的干扰，匹配正确率较低。针对这一问题，本文提出了一种利用与车辆轨迹同步采集的图像对行驶场景进行分类，从而辅助城市复杂道路环境下地图匹配的方法。该方法在车辆靠近高架区域时利用图像对车辆行驶场景进行分类，结合车辆行驶方向、轨迹点与路段的距离、匹配路段邻接性等指标，对当前轨迹点进行实时匹配。以上海市三段高频采集的轨迹数据为例进行试验，使用匹配率、召回率、精确率等指标对结果进行精度评价。结果表明，本文方法的平均匹配率、召回率和精确率达到96.86%、97.17%、93.46%，优于传统实时匹配方法；对原始轨迹进行降采样后，匹配率、召回率、精确率等指标保持稳定。比较高架道路、立交等复杂场景的匹配效果，以及对比单点匹配耗时、延时和内存占用情况，本文方法均能保持较好的匹配结果。

关键词: 地图匹配, 行驶场景分类, GNSS轨迹

Abstract: Real-time map matching plays a critical role in intelligent transportation and autonomous driving. For complex road networks like elevated roads and overpasses, existing real-time matching algorithms have relatively lower accuracy due to the interference of parallel roads. Thus, a real-time map matching method combined with driving image classification is proposed. When the vehicle nears the elevated roads, the current trajectory point is matched by combining the scenario classification result with the vehicle’s heading direction, the distance to the road segment, and the adjacency with the previous matching segment. For the experiment, three trajectories with high GNSS sampling rates were collected in Shanghai. Three indicators (match rate, recall, and precision) are used to evaluate the matching performance. The results show that the average matching rate, recall, and precision of the proposed method are 96.86%, 97.17%, 93.46%, which outperform the traditional real-time matching methods. As the sampling interval increases, the proposed method still performs well with three indicators. Comparing the matching results in complex areas such as elevated roads and intersections, as well as comparing the matching time, latency and memory consumption, this method can maintain good matching results.

Key words: map-matching, driving scenario classification, GNSS trajectory

中图分类号:

P237

傅琛, 黄升钶, 汤焱, 吴杭彬, 刘春, 姚连璧, 黄炜. 结合行驶场景语义的轨迹-路网实时匹配方法[J]. 测绘学报, 2021, 50(11): 1617-1627.

FU Chen, HUANG Shengke, TANG Yan, WU Hangbin, LIU Chun, YAO Lianbi, HUANG Wei. A real-time map matching method for road network using driving scenario classification[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(11): 1617-1627.

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结合行驶场景语义的轨迹-路网实时匹配方法

A real-time map matching method for road network using driving scenario classification

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