Journal of Jilin University(Engineering and Technology Edition) ›› 2024, Vol. 54 ›› Issue (5): 1332-1346.doi: 10.13229/j.cnki.jdxbgxb.20220830

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Cooperative control method for intelligent networked vehicles in ramp confluence area

Qing-lu MA1(),Hao YAN1,Zhen-yu NIE1,Yang-mei LI2   

  1. 1.School of Traffic and Transportation，Chongqing Jiaotong University，Chongqing 400074，China
    2.Ningxia Jiaotou Expressway Management Co. ，Ltd. ，Yinchuan 750000，China
  • Received:2022-06-28 Online:2024-05-01 Published:2024-06-11

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Abstract:

In order to solve the efficiency and safety problems of vehicle passage during ramp confluence in an intelligent network environment， a cooperative control method for intelligent vehicles based on game theory was proposed. In this method， a three-way game with vehicles on the outside of the ramp as the main body was constructed， and the vehicle revenue function was established from three aspects of safety， lane change and vehicle efficiency. Based on Shapley's value， the optimal control strategy of the vehicle in a three-way game was obtained. In the experiment， the merging area of Chongqing Yuma Road and Baotou direction of Chongqing Inner Ring Expressway was selected as the prototype of the road network， and the vehicle running scene in the merging area was co-simulated by SUMO/TraCI and Python. The results show that compared with the evolutionary game control method， the overall average travel time ratio is reduced by 3.22%， and the average delay time is reduced by 14.47%，the time-to-collision （TTC） less than 3 s decreased by 26.56%. It effectively reduces the traffic conflict time and improves the traffic operation efficiency in the confluence area.

Key words: traffic engineering, Nash equilibrium, cooperative game control, ramp confluence area, intelligent networked

CLC Number: 

  • U491.54

Fig.1

Cooperative game cooperative control system"

Fig.2

Experimental road"

Fig.3

Three-party third-order game matrix"

Table 1

Driver C1 comprehensive income distribution"

S［C1］［C1， C2］［C1， C3］［C1， C2， C3］
策略x2， x2， x2x1， y1， x2x2， x2，x2x1， y1， x2；x1， x1， y1
V（S）F1（t）F1（t）+F2（t）F1（t）+F3（t）F1（t）+F2（t）+F3（t）
V（S-i）0F' 2（t）F' 3（t）F' 2（t）+F' 3（t）
V（S）-V（S-i）F1（t）F1（t）+F2（t）-F' 2（t）F1（t）+F3（t）-F' 3（t）F1（t）+F2（t）+F3（t）-F' 2（t）-F' 3（t）
|S|1223
W（|S|）1/31/61/61/3

Fig.4

Influence of q value on lane change safety clearance"

Fig.5

Influence of different weights on the game solution"

Fig.6

Vehicle traffic efficiency comparison"

Table 2

Vehicle traffic efficiency"

参数fp1fp2fp3fp4fp5fq1fq2fq3fq4fq5
平均行程时间/s33.9231.8327.0925.3925.2431.2230.3025.9725.9225.44
平均延误时间/s11.178.844.982.922.538.517.673.903.502.76

Fig.7

Improved on-ramp vehicle speed variation"

Fig.8

Improved front ramp L1 vehicle speed fluctuation"

Fig.9

Improved front ramp L2 vehicle speed fluctuation"

Fig.10

Improved back ramp vehicle speed variation"

Fig.11

Improved back ramp L1、L2 traffic flow vehicle speed fluctuation"

Fig.12

Main traffic flow vehicle speed changes"

Fig.13

Lane 1 and Lane 2 vehicle travel time changes"

Fig.14

Vehicle conflict comparison"

Table 3

Vehicle collision time normality and difference test"

编号均值/s偏度峰度统计量秩平均值
13.231.683.360.831268.08
23.101.743.680.831247.32
32.371.794.470.82881.67
44.491.161.390.911553.75
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