顾及数字地图中车道走向的车辆跟踪增强算法

doi:10.11947/j.AGCS.2021.20210258

测绘学报 ›› 2021, Vol. 50 ›› Issue (11): 1522-1533.doi: 10.11947/j.AGCS.2021.20210258

顾及数字地图中车道走向的车辆跟踪增强算法

庄瀚洋^1,2, 王晓亮^3,4,5, 王春香^3,4,5, 杨明^3,4,5

1. 上海交通大学密西根学院, 上海 200240;
2. 广西科技大学广西汽车零部件与整车技术重点实验室, 广西柳州 545006;
3. 上海交通大学自动化系, 上海 200240;
4. 系统控制与信息处理教育部重点实验室, 上海 200240;
5. 上海工业智能管控工程技术研究中心, 上海 200240

收稿日期:2021-05-11 修回日期:2021-09-27 发布日期:2021-12-07
通讯作者: 王春香 E-mail:wangcx@sjtu.edu.cn
作者简介:庄瀚洋(1989—),男,博士,助理研究员,研究方向为智能网联车辆。
基金资助:
广西汽车零部件与整车技术重点实验室开放研究课题（2020GKLACVTKF02）；国家自然科学基金联合基金项目（U1764264）

Vehicle tracking enhancement based on the lane orientation priori from digital maps

ZHUANG Hanyang^1,2, WANG Xiaoliang^3,4,5, WANG Chunxiang^3,4,5, YANG Ming^3,4,5

1. University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China;
2. Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou 545006, China;
3. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China;
4. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China;
5. Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai 200240, China

Received:2021-05-11 Revised:2021-09-27 Published:2021-12-07
Supported by:
Guangxi Key Laboratory of Automobile Components and Vehicle Technology Research Project (No. 2020GKLACVTKF02);The National Natural Science Foundation of China (No. U1764264)

摘要/Abstract

摘要： 车辆跟踪技术旨在从连续场景中估计目标车辆的状态，对智能车辆的环境感知、场景理解和目标行为预测起着至关重要的作用。基于激光雷达的感知系统能够提供准确的车辆检测结果，但依据检测结果进行车辆跟踪时，存在车辆朝向估计失准导致跟踪误差大、轨迹预测稳定性差的难题，尤其在目标距离较远、点云较为稀疏的情况下。考虑到大多数时刻车辆行驶方向与车道线方向基本一致，本文提出一种基于数字地图中车道朝向先验信息的车辆跟踪增强方法，将局部车道线的识别结果与OpenStreetMap地图中的车道线信息进行融合，建立道路模型并获取道路朝向的先验约束；在基于扩展卡尔曼滤波的车辆跟踪框架下，利用该约束优化车辆的朝向估计，进而提升车辆跟踪的精度与轨迹预测稳定性。在KITTI数据集上的定性与定量试验证明，本文所提出的方法在多目标跟踪指标上提升至少0.33%，平均位移误差降低了0.014 m以上，同时，对于60 m外车辆目标的跟踪误差降低了0.08 m以上。

关键词: 数字地图, 车辆跟踪, 扩展卡尔曼滤波, 智能车辆

Abstract: Vehicle tracking aims at estimating the target vehicle state from continuous temporal measurement. It is the core for intelligent vehicle to understand the environment and predict the targets’ behaviors. LiDAR-based perception system of intelligent vehicle provides precise vehicle detection results, which are the basis of vehicle tracking. However, the tracking process suffers from issues of orientation mis-estimation and low stability of tracking trace, especially when the target vehicles are far away from LiDAR. The sparseness of point cloud at long distance is the key problem. Therefore, this paper proposes an enhanced vehicle tracking method based on the lane orientation priori from a digital map. It utilizes the OpenStreetMap digital map to fuse with the local lane markers detection results. The road model is built to obtain the constraint of lane orientation. Based on the vehicle tracking method built on extended Kalman filter, this lane orientation constraint is utilized to improve the vehicle orientation estimation. Consequently, the vehicle tracking accuracy and stability can then be reached. The tracking result indicates the multiple objects tracking accuracy can be increased by 0.33% while the average translation error can be reduced by at least 0.014 m. Moreover, the target vehicle at 60 m away from the host vehicle can be improved to reduce the error of 0.08 m.

Key words: digital map, vehicle tracking, extended kalman filter, intelligent vehicle

中图分类号:

P208

庄瀚洋, 王晓亮, 王春香, 杨明. 顾及数字地图中车道走向的车辆跟踪增强算法[J]. 测绘学报, 2021, 50(11): 1522-1533.

ZHUANG Hanyang, WANG Xiaoliang, WANG Chunxiang, YANG Ming. Vehicle tracking enhancement based on the lane orientation priori from digital maps[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(11): 1522-1533.

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顾及数字地图中车道走向的车辆跟踪增强算法

Vehicle tracking enhancement based on the lane orientation priori from digital maps

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