Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (7): 1923-1934.doi: 10.13229/j.cnki.jdxbgxb.20221225

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Perceptual fusion method of vehicle road cooperation roadside unit in superhighway

Yong-ming HE1,2(),Cong QUAN1,2,Kun WEI3(),Jia FENG1,Ya-nan WAN1,Shi-sheng CHEN1   

  1. 1.College of Civil Engineering and Transportation，Northeast Forestry University，Harbin 150000，China
    2.Engineering Consulting and Design Co. ，Ltd. ，Northeast Forestry University，Harbin 150000，China
    3.Key Laboratory of Road Structure and Material Transportation Industry，Chang'an University，Xi'an 710000，China
  • Received:2022-09-23 Online:2024-07-01 Published:2024-08-05
  • Contact: Kun WEI E-mail:hymjob@nefu.edu.cn;weikun@chd.edu.cn

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Abstract:

To improve the driving safety of the superhighway and verify the feasibility of multi-sensor fusion algorithm in the superhighway， starting from the roadside sensing unit in vehicle-road cooperation， a multi-sensor perception fusion system model was established by using PreScan. The advantages and disadvantages of different methods were analyzed by understanding the types and architectures of multi-sensor fusion methods based on the multi-sensor fusion method. The hybrid multi-sensor information data fusion was adopted as the overall architecture， and the back-end fusion was adopted as the way to process and analyze data. This method improved the detection accuracy of target distance and speed of sensors on superhighways. The maximum improvement of distance and speed of Camera can reached 7.2% and 36.8%， respectively. The research results demonstrate that the method improve the detection capability of roadside units， and verify the feasibility and effectiveness of the adaptive weighted fusion algorithm on the superhighway.

Key words: engineering of communications and transportation system, fusion algorithm, superhighway, roadside perception, vehicle-infrastructure cooperation

CLC Number: 

  • U495

Fig.1

Working principle diagram of vehicle road coordination system"

Fig.2

Flowchart of different integration methods"

Table 1

Comparison of characteristics of multi-sensor information fusion methods"

融合名称损失程度难度精度实时性容错率抗干扰依赖性代价复杂程度
数据级ⅢⅠⅢⅠⅠⅠⅠⅠⅠ
特征级ⅡⅡⅡⅡⅡⅡⅡⅢⅡ
决策级ⅠⅢⅠⅢⅢⅢⅢⅡⅢ

Fig.3

Front end integration mode"

Fig.4

Back end integration mode"

Fig.5

Flow chart of hybrid architecture"

Fig.6

Technical roadmap of multi-sensor fusion"

Fig.7

Adaptive weighted fusion algorithm model"

Fig.8

Matrix scanning operation mode comparison diagram"

Table 2

Type of TIS sensor mode"

模式类型横切面类型物体被测量的位置最大输出目标
Pencil纯光线点检测基于射线与物体交点处单个
Pyramid棱锥矩形检测基于找到的最近距离多个
Cone圆锥圆检测基于找到的最近距离多个
Elliptical cone椭圆锥椭圆检测基于找到的最近距离多个

Fig.9

Pattern diagram of different types of rays in TIS sensor"

Fig.10

Configuration scheme of multi-sensor fusion system for superhighway"

Fig.11

Distance fusion data comparison chart"

Fig.12

Speed fusion data comparison chart"

Fig.13

Comparison diagram of target state distance error"

Fig.14

Comparison diagram of target state speed error"

Table 3

Error comparison table of multi-sensor perception fusion algorithm"

采样类型采样时间/s采样方法
TISCameraRadarNew
距离0.0~3.1Ⅱ——Ⅱ
3.1~4.2ⅡⅠ—Ⅲ
4.2~5.4————
速度0.0~2.9Ⅱ—ⅠⅢ
2.9~3.8ⅠⅠⅡⅢ
3.8~5.4————

Fig.15

Accuracy of range detection improvement"

Fig.16

Increased accuracy of speed detection"

Table 4

Error comparison table of multi-sensor perception fusion algorithm"

采样方法
采样类型TISCameraRadar
距离探测7.22.8-
速度探测11.136.89.1
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