联合博弈论与驾驶风格的混合交通流变道决策模型

doi:10.13229/j.cnki.jdxbgxb.20240537

Abstract

Abstract:

To address the issue of inaccuracy of vehicle lane change decision in the mixed traffic flow scenario of HV and AV， a vehicle lane change decision model was proposed. The model is based on game theory， multiple game functions were constructed for the continuous cooperative lane change situation of adjacent vehicles， the influence of communication uncertainty was eliminated by Harsanyi transform， and the driving style of vehicles was distinguished by K-means++ clustering， the game returns were further adjusted by risk factors. The lane change model was verified by using the SUMO simulation platform. The experimental results showed that under the fixed AV permeability， the average passing number of vehicles is effectively increased and the average passing time is reduced by applying the combined game theory and driving style lane change model， at the same time， no accidents occurred in the test， which proves the stability and safety of the lane change model. Under different AV permeability， the average passing time of vehicles decreases significantly with the increase of permeability， which indicates that the AV can effectively utilize the lane.

Key words: game theory, Harsanyi transform, driving style, K-means++ clustering, lane change decision

CLC Number:

U491

Xin-gang GUO,Song WANG,Chao CHENG,Zhen FAN. Combined game theory and driving style hybrid traffic flow lane change decision model[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(12): 3875-3884.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Table 1

Payoff matrix of main car game"

后随车D策略

主车A

概率

等待

变道

加速

减速

变道

不变

$α P 110 + β Q 110 + τ 11$

$α P 211 + β Q 211 + α U V + U C + τ 21$

$α P 211 + β Q 221 + α U V + U C + τ 22$

$α P 211 + α U V + U C + τ 23$

$α P 211 + β Q 241 + α U V + U C + τ 24$

$n 1$

$n 2$

$n 3$

$1 - n 1 - n 2 - n 3$

概率

m

1 - m

Table 1

Table 2

Payoff matrix of the following car game"

后随车D策略

主车A

概率

等待

变道

加速

减速

变道

不变

$α P 311 + β Q 311 + U C + τ 31$

$α P 320 + β Q 320 + U C + τ 32$

$r Γ$

$α P 340 + β Q 341 + τ 34$

$α P 411 + β Q 311 + α U V + U C + τ 41$

$α P 420 + β Q 320 + α U V + U C + τ 42$

$r Γ$

$α P 440 + β Q 341 + α U V + τ 44$

$n 1$

$n 2$

$n 3$

$1 - n 1 - n 2 - n 3$

概率

m

1 - m

Table 2

Table 3

Payoff matrix of repeated games"

J车策略

J车收益

D车收益

加速

减速

变道

不变

$α P 511 + β Q 511 + α U V + U C + τ 51$

$α P 510 + β Q 520 + α U V + U C + τ 52$

$r Ω$

$α P 540 + β Q 541 + α U V + τ 54$

$α P 611 + β Q 611 + α U V + U C + τ 61$

$α P 611 + β Q 621 + α U V + U C + τ 62$

$α P 611 + α U V + U C + τ 63$

$α P 611 + β Q 641 + α U V + U C + τ 64$

Table 3

Table 4

Table 5

Fig.4

Table 6

Fig.5

Fig.6

Table 7

Table 8

Fig.7

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成分	特征值	贡献率/%	累计贡献率/%
1	3.788	24.530 2	24.530 2
2	2.698	17.468 3	41.998 5
3	2.425	15.702 6	57.701 1
4	2.007	12.992 9	70.694 0
5	1.561	10.105 3	80.799 3
6	1.239	8.023 8	88.823 1
7	0.544	3.525 6	92.348 7
8	0.444	2.876 5	95.225 2
9	0.294	1.902 4	97.127 6
10	0.168	1.090 1	98.217 7
11	0.092	0.567 4	98.785 1
12	0.064	0.415 7	99.200 8
13	0.059	0.386 3	99.587 1
14	0.034	0.213 6	99.800 7
15	0.012	0.089 6	99.890 3
16	0.011	0.073 2	99.963 5
17	0.005	0.036 5	100.000 0

参数	数值
车辆长度/m	4.2
最高速度/（m·s^-1）	30
最大加速度/（m·s^-2）	3.3
最大减速度/（m·s^-2）	4.2

车辆密度/（veh·h^-1）	A组	B组	C组	D组
1 000	（0，0）	（0，0）	（0，0）	（0，0）
1 500	（0，0）	（0，0）	（0，0）	（0，0）
2 000	（1，0）	（0，0）	（1，0）	（0，0）
2 500	（1，1）	（1，0）	（1，1）	（0，0）
3 000	（2，1）	（2，1）	（2，1）	（0，0）
3 500	（2，2）	（2，1）	（3，2）	（0，0）
4 000	（3，2）	（2，2）	（4，4）	（0，0）
4 500	（3，2）	（2，2）	（6，5）	（1，0）

实验组	平均通过时间/s	通过数量/辆
1	（96.78，90.35，82.56）	（51，54，60）
2	（102.54，98.72，90.21）	（67，72，90）
3	（112.43，102.57，97.35）	（89，96，108）
4	（139.56，128.33，120.49）	（111，126，135）
5	（151.28，141.71，129.65）	（134，153，165）
6	（176.12，173.49，156.53）	（157，177，196）
7	（191.53，180.52，170.02）	（193，214，232）
8	（212.36，203.66，181.18）	（221，223，241）

Combined game theory and driving style hybrid traffic flow lane change decision model

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