Enhanced localization system based on camera and lane markings

被引：0
|
作者：
He K. [1 ]
Ding H.-T. [1 ]
Xu N. [1 ]
Guo K.-H. [1 ]
机构：
[1] College of Automotive Engineering, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2023年 / 53卷 / 03期
关键词：
camera; lane marking; localization; map matching; sensor fusion; vehicle engineering;
D O I：
10.13229/j.cnki.jdxbgxb20220895
中图分类号：
学科分类号：
摘要：
In the traditional technology of lateral localization using lane markings identified by cameras，incorrect matching of lanes or their left and right boundary points causes large localization errors under driving conditions such as lane changing. A multi-indicator weighted evaluation map matching algorithm combined with lane change recognition method was proposed. Further，a lane left and right boundary point determination method was designed，and a camera-based bilateral boundary line lateral localization method was proposed based on accurate matching to lanes and their boundary points，which can improve the lateral localization accuracy relative to lanes，and then fuse the camera with GPS，IMU，wheel odometer，and lightweight lane level map to form a complete localization system. The experimental results show that the accuracy and stability are significantly improved compared with the traditional fusion localization method using cameras and lane markings. The localization system designed in this paper provides a low-cost and high-accuracy solution for autonomous driving localization. © 2023 Editorial Board of Jilin University. All rights reserved.
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页码：663 / 673
页数：10
相关论文
共 21 条
  • [1] Gao Zhen-hai, Sun Tian-jun, He Lei, Causal reasoning decision⁃making for vehicle longitudinal automatic driving, Journal of Jilin University (Engineering and Technology Edition), 49, 5, pp. 1392-1404, (2019)
  • [2] Jo K, Kim J, Kim D, Et al., Development of autonomous car-part I: distributed system architecture and development process, IEEE Transactions on Industrial Electronics, 61, 12, pp. 7131-7140, (2014)
  • [3] Lu W, Rodriguez F S A, Seignez E, Et al., Lane marking-based vehicle localization using low-cost GPS and open source map, Unmanned Systems, 3, 4, pp. 239-251, (2015)
  • [4] Xu L H, Hu S G, Luo Q., A new lane departure warning algorithm considering the driver's behavior characteristics, Mathematical Problems in Engineering, 8, pp. 1-11, (2015)
  • [5] Levinson J, Montemerlo M, Thrun S., Map-based precision vehicle localization in urban environments, Robotics: Science and Systems, 3, pp. 121-128, (2007)
  • [6] Qin Xiao-hui, Wang Zhe-wen, Pang Tao, Et al., Vehicle positioning method based on tight coupling of vehicle model in enclosed environments, Automotive Engineering, 43, 9, pp. 1328-1335, (2021)
  • [7] Kubo N, Suzuki T., Performance improvement of RTK-GNSS with IMU and vehicle speed sensors in an urban environment, IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 99, 1, pp. 217-224, (2016)
  • [8] Hata A Y, Wolf D F., Feature detection for vehicle localization in urban environments using a multilayer LIDAR, IEEE Transactions on Intelligent Transportation Systems, 17, 2, pp. 420-429, (2015)
  • [9] Castorena J, Agarwal S., Ground-edge-based LIDAR localization without a reflectivity calibration for autonomous driving, IEEE Robotics and Automation Letters, 3, 1, pp. 344-351, (2017)
  • [10] Wolcott R W, Eustice R M., Robust LIDAR localization using multiresolution Gaussian mixture maps for autonomous driving, The International Journal of Robotics Research, 36, 3, pp. 292-319, (2017)
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