面向变曲率道路的自动驾驶汽车换道博弈运动规划与协同控制研究

doi:10.19562/j.chinasae.qcgc.2023.07.001

Abstract

Abstract:

When multiple autonomous vehicles perform lane change and merging tasks on structured road， steering and merging actions need to be comprehensively considered to avoid potential accidents. Meanwhile， the changing road curvature and surrounding vehicle speed also increase the difficulty of cooperative control. Focusing on the above issues， this paper proposes a multi-vehicle lane change gaming motion planning and cooperative control method facing variable curvature road. Firstly， a multi-vehicle model in curvature coordinate system is developed to determine the inter-vehicle safety distance and dynamics state. Then， by systematically considering the road curvature variation and surrounding vehicle information， a game-based multi-vehicle lane change motion planning algorithm is proposed， which uses a distributed framework to quickly solve the optimal speed trajectory and lane change timing considering personalized driving. Finally， the road curvature and planning trajectory are identified effectively based on B-sample curve， and an adaptive time-varying model predictive control algorithm is constructed to achieve trajectory tracking. Specifically， the control parameters are updated in real time under the single-step prediction domain to eliminate the control deviations caused by frequently various vehicle speed and curvature. The co-simulation results show that the proposed method can reduce the tracking error by 58% compared to the Stanley method， with reduction of the merging time by 74% compared to the cooperative adaptive cruise control method. Moreover， the computational solution efficiency is only 10% of the centralized method.

Key words: autonomous vehicles, multi-vehicle gaming, model predictive control, motion planning for lane changing

Cheng Lin, Bowen Wang, Lü Peiyuan, Xinle Gong, Xiao Yu. Research on Motion Planning and Cooperative Control for Autonomous Vehicles with Lane Change Gaming Maneuvers Under the Curved Road[J].Automotive Engineering, 2023, 45(7): 1099-1111.

Figures/Tables 23

输入：系统虚拟队列下的车辆状态 $ξ 1 ～ ξ n$
输出：纳什均衡策略 $A * = π 1 , ?, π i , ?, π n *$
1.	初始化换道时刻 $τ 1 * ～ τ i ∈ N * = 0$
2.	while 车辆节点 $i$ 属于集合 $N$ do
3.	虚拟队列构型下选择前方距离最近的车辆节点 $l$ 作为自车的领导节点
4.	$m i n ∑ k = 0 N s l k + 1 \| t - s i k + 1 \| t - d s a f e Q 1 2 + v i k + 1 \| t - v i e Q 2 2 + a i k \| t + Δ a i k \| t Q 3 2$ 得到控制策略序列 $a i , 0 \| t ～ a i , N - 1 \| t$
5.	根据式（19）和 $a i , 0 \| t ～ a i , N - 1 \| t$ 计算车辆 $i$ 的换道博弈代价 $J i, j ∈ N o 0 \| t ～ J i, j ∈ N o N - 1 \| t$ 以及原始车道代价 $J i, j ∈ N r 0 \| t ～ J i, j ∈ N r N - 1 \| t$
6.	$C i, j ∈ N τ \| t = ∑ k = 0 k = τ J i, j ∈ N r k \| t + ∑ k = τ k = N - 1 J i, j ∈ N o k \| t τ ∈ 0 : N - 1$
7.	If $C i, j ∈ N τ \| t = m i n C i, j ∈ N 0 \| t ～ C i, j ∈ N N - 1 \| t$ then
8.	换道时刻 $τ i * = t + τ$
9.	end
10.	策略集合更新 $A * = A * ? π i *$
11.	end

参数	数值
质量 $m$ /kg	1 523
车体转动惯量 $I z$ /（kg·m²）	1 536
质心到前轴距离 $l f$ /m	1.112
质心到后轴距离 $l r$ /m	1.805
前轮侧偏刚度 $C f$ /（N·（°）^-1）	57 300
后轮侧偏刚度 $C r$ /（N·（°）^-1）	86 900
预测时域 $N$	10
安全相对距离 $d s$ /m	10
加速度约束 $a m i n$ ， $a m a x$ /（m·s^-2）	±3.5
转角约束 $δ m i n$ ， $δ m a x$ /rad	±0.35

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方法	分布式	集中式
求解器内计算矩阵个数	397	3 411
输入参数个数	29	228
求解器内变量个数	38	181

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Research on Motion Planning and Cooperative Control for Autonomous Vehicles with Lane Change Gaming Maneuvers Under the Curved Road

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References 29

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