基于双层求解策略的平行泊车轨迹规划

doi:10.19562/j.chinasae.qcgc.2023.12.012

Abstract

Abstract:

Trajectory planning plays a key role in shortening parking time and reducing tracking difficulty， as it connects the upper-level perception and the lower-level control in parking systems. However， it is challenging to balance trajectory quality， generalization ability， and computational efficiency in parallel parking trajectory planning. To address this issue， the parallel parking trajectory planning based on double-layer solution strategy （DLSS） is proposed. The strategy includes two layers： in the first layer， the parallel parking path is divided into two segments connected by anchor points. The anchor point is identified through a backward path planning approach. The paths from the endpoint to the anchor point and from the anchor point to the starting point are planned separately. Then， a "time-optimal" profile is added to the path， and the state and control variables at specific time points are obtained in reverse order. In the second layer， the simultaneous orthogonal configuration method is used to transform the continuous state and control variables in the parallel parking optimal control into discrete variables in the trajectory nonlinear programming. The state and control variables obtained in the first layer are used as the initial values for the nonlinear programming to obtain numerical solutions. Five parallel parking scene models are established and simulated， and the results show that the optimal trajectory that meets the constraints requirements can be planned for different parking starting postures and parking space sizes， which improves the generation quality and generalization ability of trajectories， and has satisfactory computational efficiency.

Key words: trajectory planning, double-layer solution strategy, parallel parking, numerical optimization

Hongchang Zhang,Peng Ning,Jie Yang,Jianwei Song,Lin Hao,Juan Zeng. Parallel Parking Trajectory Planning Based on Double-Layer Solution Strategy[J].Automotive Engineering, 2023, 45(12): 2299-2309.

Figures/Tables 17

场景	起始位姿和车位尺寸
场景二	$x t 0 = 1 y t 0 = 2 θ t 0 = - 0.2 v t 0 = 0 φ t 0 = 0 ? a n d ? C L = 4 S L = 6 S W = 2.5 ? a n d ? a t 0 = 0 w t 0 = 0$
场景三	$x t 0 = 7 y t 0 = 1.8 θ t 0 = - 0.2 v t 0 = 0 φ t 0 = 0 ? a n d ? C L = 4 S L = 6 S W = 2.5 ? a n d ? a t 0 = 0 w t 0 = 0$
场景四	$x t 0 = - 5 y t 0 = 1.8 θ t 0 = 0.2 v t 0 = 0 φ t 0 = 0 ? a n d ? C L = 4 S L = 5.8 S W = 2.5 ? a n d ? a t 0 = 0 w t 0 = 0$
场景五	$x t 0 = - 5 y t 0 = 2 θ t 0 = 0 v t 0 = 0 φ t 0 = 0 ? a n d ? C L = 4 S L = 5.6 S W = 2.5 ? a n d ? a t 0 = 0 w t 0 = 0$

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参数	符号	单位	数值
车辆轴距	L	m	2.830
车辆前悬	L_f	m	1.006
车辆后悬	L_r	m	1.070
车辆宽度	W	m	1.862
泊车最大车速	v_max	m/s	1.0
泊车最大加速度	a_max	m/s²	0.5
车辆最大前轮转角	φ_max	rad	0.576
车辆最大前轮转向角速度	ω_max	rad/s	0.576

参数	符号	单位	数值
车辆起始位置（水平方向）	x_t₀	m	-5
车辆起始位置（垂直方向）	y_t₀	m	2
车辆起始航向角	θ_t₀	rad	0
车辆起始速度	v_t₀	m/s	0
车辆起始前轮转角	φ_t₀	rad	0
车位长	SL	m	6
车位宽	SW	m	2.5
道路宽度	CL	m	4

方法	没有初值	双层求解策略				文献［18］
方法	CPU time	初始路径CPU time	CPU time	CPU time*	泊车时间	CPU time	CPU time*	泊车时间
场景一	失败	0.311 s	5.725 s	0.866 s	24.20 s	失败	6.524 s	25.56 s
场景二	失败	0.173 s	5.226 s	0.758 s	18.94 s	失败	失败	失败
场景三	失败	0.102 s	5.318 s	0.792 s	16.22 s	失败	7.112 s	25.96 s
场景四	失败	0.241 s	5.882 s	0.924 s	25.21 s	失败	9.189 s	25.67 s
场景五	失败	0.272 s	6.021 s	1.198 s	32.23 s	失败	失败	失败

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Parallel Parking Trajectory Planning Based on Double-Layer Solution Strategy

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