Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 772-780.doi: 10.13229/j.cnki.jdxbgxb20221450

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Localization algorithm of vehicular sensor based on multi⁃mode interaction

Jian ZHANG1(),Jin-bo LIU1,Yuan GAO1,Meng-ke LIU1,Zhen-hai GAO2(),Bin YANG2   

  1. 1.Department of Intelligent Driving，Intelligent Connected Vehicle R&D Institute of China FAW，Changchun 130062，China
    2.State Key Laboratory of Automotive Simulation and Control，Jilin University，Changchun 130022，China
  • Received:2022-11-14 Online:2023-03-01 Published:2023-03-29
  • Contact: Zhen-hai GAO E-mail:zhangjian3@faw.com.cn;gaozh@jlu.edu.cn

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Abstract:

A multi-mode interactive filtering algorithm that integrates Global Positioning System （GPS） and vehicle sensor information was proposed， so that the vehicle can adapt to a variety of driving conditions. The longitudinal and lateral motion model were built to describe the full coverage prediction of the expected driving position of the vehicle. The credibility evaluation of GPS signal was realized by data-driven neural network and vehicle motion model. The accurate estimation of process error and measurement error was realized by interactive filtering and probability updating between vehicle motion models. The positioning fusion algorithm was verified in the real vehicle testing environment. The results show that the proposed algorithm can improve the positioning accuracy of the multi-sensor positioning system and provide more accurate position estimation results in the case of unstable signals.

Key words: vehicle engineering, signal credibility evaluation, interacting multiple model, fusion positioning

CLC Number: 

  • U463.67

Fig.1

System chart"

Fig.2

Test data acquisition"

Fig.3

Linear regression analysis diagram of neural network training"

Fig.4

Vehicle kinematics model"

Fig.5

Schematic diagram of IMM-KF algorithm"

Fig.6

Kalman filtering process"

Fig.7

Related physical picture of experimental sample vehicle"

Table 1

Related parameters of experimental equipment of IMM-KF test platform"

实验设备型号精度
控制器MicroAutoboxII控制器-
LEN-GZ1903RTK模式

水平精度：0.01 m

航向精度：0.15°

DGNSS模式

水平精度：0.60 m

航向精度：1.00°

Fig.8

Curve of vehicle speed change"

Fig.9

Comparison of positioning information in straight driving condition"

Fig.10

Comparison of positioning information in straight driving condition（partial enlarged detail）"

Fig.11

Curve of positioning error"

Fig.12

Comparison of trajectory curvature"

Fig.13

Curve of vehicle speed change"

Fig.14

Comparison of positioning information of lane change condition"

Fig.15

Curve of positioning error"

Fig.16

Comparison of trajectory curvature change"

Fig.17

Results of composite test scenario"

Fig.18

Curve of positioning error"

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