Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 746-757.doi: 10.13229/j.cnki.jdxbgxb20220560

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Integrated lane⁃changing model of decision making and motion planning for autonomous vehicles

Xue XIAO1(),Ke-ping LI1,Bo PENG2,Man-wei CHANG1   

  1. 1.Key Laboratory of Road and Traffic Engineering,Ministry of Education,Tongji University,Shanghai 201804,China
    2.College of Transportation,Jilin University,Changchun 130012,China
  • Received:2022-05-12 Online:2023-03-01 Published:2023-03-29

Abstract:

Decision-making and motion planning are the crucial modules of autonomous vehicles. Focusing on the driving parameter relationship between decision making module and motion planning module, and the dynamic characteristics of new hybrid traffic flow, lane-changing model of autonomous vehicles was established. The decision-making process of lane-changing of autonomous vehicles was described by Stackelberg game theory, and the game cost function is quantified by negative exponential function. The decision results of the autonomous vehicle were taken as one of the inputs of the motion planning module. Polynomial was used to describe the lateral and longitudinal trajectories of the vehicle, and simulated annealing algorithm was used to find the optimal trajectory. The simulation results show that the proposed decision programming model can quickly generate safe, comfortable and feasible trajectory.

Key words: engineering of communications and transportation system, game theory, motion planning, simulated annealing

CLC Number: 

  • U491.2

Table 1

Game strategies"

代价函数跟随车辆
加速a减速d
领导车辆换道c(Uav,c,Uhv,a)(Uav,c,Uhv,d)
跟驰f(Uav,f,Uhv,a)(Uav,c,Uhv,d)

Table 2

Cost function of motion planning"

含义权重代价分量
安全性wcCc=1/0tD
舒适性wJxCJx=0tL?A2tdt
wJyCJy=0tL?O2tdt
高效性woCo=0tLO2tdt
wvCv=L˙At-vmax2
wtCt=t

Fig.1

Decision making and motion planning workflow"

Fig.2

Simulation scenario and initial positions of vehicles"

Table 3

Driving parameter settings of vehicles"

参 数数 值
车辆长度/m4.2
车辆宽度/m2
最小碰撞半径/m3
加速度/(m·s-2[-3,3]
车辆可行速度/(m·s-1[0,17]
智驾车速度/(m·s-112.5
智驾车加速度/(m·s-20
智驾车驾驶特性参数0.5,0.3,0.2
车1速度/(m·s-112.5
车1加速度/(m·s-20
车1驾驶特性参数0.1,0.1,0.8
车2速度/(m·s-111
车2加速度/(m·s-20
车2驾驶特性参数0.3,0.5,0.2
车3速度/(m·s-112.5
车3加速度/(m·s-20
车3驾驶特性参数0.5, 0.3, 0.2

Fig.3

Driving parameters when vehicle1 is conservative and AV is normal"

Fig.4

Heat map of game cost"

Fig.5

Driving parameters when vehicle1 is aggressive and AV is normal"

Fig.6

Driving parameters comparison between real lane changing process and AV"

Fig.7

Comparison of feasible solution space between ergodic method, PSO, and SA"

Table 4

Setting of sampling space for trajectory planning"

参 数取值
横向位置采样 范围/m左转[1.8,5.4]
直行[-1.8,1.8]
右转[-5.4,-1.8]
横向位置采样间隔/m0.1
规划时间采样范围/s45
规划时间采样间隔/s0.1
横向加速度采样范围/(m·s-2[-3,3]
横向加速度采样间隔/(m·s-20.3
实际规划时间长度/s0.1

Table 5

Parameters setting of SA"

参数数值
初始温度100
马尔科夫链长度5
温度下降率0.9
结束温度3

Table 6

Parameters setting of particle swarm optimization"

含义参数值
种群数量5
最大迭代次数20
惯性因子0.95
加速常数3

Fig.8

Comparison of simulation running time"

Fig.9

Convergence process comparison of SAPSO, PSO, and SA model"

Table 7

Simulation results of different SA parameters"

组别

起始

温度

终止

温度

降温比例0.9降温比例0.95
最优解耗时/s最优解耗时/s
17011.021.861.312.02
28011.291.711.222.06
39011.021.801.151.80
410011.051.821.171.77
511011.231.881.311.78
612011.061.721.221.82
713011.181.781.391.97
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