基于决策-规划迭代框架的智驾车换道行为建模

doi:10.13229/j.cnki.jdxbgxb20220560

Abstract

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

Xue XIAO,Ke-ping LI,Bo PENG,Man-wei CHANG. Integrated lane⁃changing model of decision making and motion planning for autonomous vehicles[J].Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 746-757.

Figures/Tables 16

Table 1

Game strategies"

代价函数		跟随车辆
代价函数		加速 $a$	减速 $d$
领导车辆	换道 $c$	$(U a v, c, U h v, a)$	$(U a v, c, U h v, d)$
领导车辆	跟驰 $f$	$(U a v, f, U h v, a)$	$(U a v, c, U h v, d)$

Table 1

Table 2

Cost function of motion planning"

含义	权重	代价分量
安全性	$w c$	$C c = 1 / ∑ 0 t D$
舒适性	$w J x$	$C J x = ∫ 0 t L ? A 2 t d t$
舒适性	$w J y$	$C J y = ∫ 0 t L ? O 2 t d t$
高效性	$w o$	$C o = ∫ 0 t L O 2 t d t$
	$w v$	$C v = L ˙ A t - v m a x 2$
	$w t$	$C t = t$

Table 2

Fig.1

Fig.2

Table 3

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Table 4

Table 5

Table 6

Fig.8

Fig.9

Table 7

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参数	数值
车辆长度/m	4.2
车辆宽度/m	2
最小碰撞半径/m	3
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^-1）	12.5
智驾车加速度/（m·s^-2）	0
智驾车驾驶特性参数	0.5，0.3，0.2
车1速度/（m·s^-1）	12.5
车1加速度/（m·s^-2）	0
车1驾驶特性参数	0.1，0.1，0.8
车2速度/（m·s^-1）	11
车2加速度/（m·s^-2）	0
车2驾驶特性参数	0.3，0.5，0.2
车3速度/（m·s^-1）	12.5
车3加速度/（m·s^-2）	0
车3驾驶特性参数	0.5， 0.3， 0.2

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/m		0.1
规划时间采样范围/s		［4，5］
规划时间采样间隔/s		0.1
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^-2）		0.3
实际规划时间长度/s		0.1

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

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

组别	起始温度	终止温度	降温比例0.9		降温比例0.95
组别	起始温度	终止温度	最优解	耗时/s	最优解	耗时/s
1	70	1	1.02	1.86	1.31	2.02
2	80	1	1.29	1.71	1.22	2.06
3	90	1	1.02	1.80	1.15	1.80
4	100	1	1.05	1.82	1.17	1.77
5	110	1	1.23	1.88	1.31	1.78
6	120	1	1.06	1.72	1.22	1.82
7	130	1	1.18	1.78	1.39	1.97

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

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