Lane-Changing Trajectory Planning Strategy for Autonomous Vehicles on Superhighways

doi:10.12141/j.issn.1000-565X.230339

Abstract

Abstract:

To improve the driving safety of autonomous vehicles on superhighways, this paper proposed a lane-changing trajectory planning strategy. Firstly, five polynomials were used to generate general lane-changing trajectory clusters, and the trajectory planning problem was quantified as the duration of solving lane-changing behavior with the limit of vehicle dynamics and surrounding traffic vehicles. Then, considering the constraints of vehicle dynamics, the vehicle dynamics model and Brush tire model were established. Based on the tire lateral force data of the established vehicle model, the tire lateral stiffness was solved, and the magic tire model was used to verify the tire lateral stiffness. Next, the phase plane of sideslip angle and yaw rate was introduced to obtain the safe driving envelope of high-speed vehicle. CarSim simulation training was carried out on given multiple groups of vehicle speeds and adhesion coefficients to determine the shortest lane-changing time that meets the vehicle dynamics constraints. Finally, considering the collision avoidance constraints with surrounding traffic vehicles, three typical lane-changing scenarios were analyzed. The shortest and longest lane-changing durations satisfying the collision avoidance requirements were determined based on the position of single obstacle vehicle, and the threshold model of lane-changing duration satisfying the safe lane-changing requirements was established. The multi-parameter safety lane-changing domain test shows that the established vehicle safety lane-changing duration boundary model can solve the safe and feasible lane-changing trajectory under the given parameters, provide trajectory reference for the superhighway lane-changing behavior, and improve the safety of the superhighway lane-changing behavior.

Key words: autonomous driving, cornering stability, trajectory planning, superhighway, modeling and simulation

CLC Number:

U463.4

HE Yongming, XING Wanyu, WEI Kun, et al. Lane-Changing Trajectory Planning Strategy for Autonomous Vehicles on Superhighways[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(4): 104-113.

Figures/Tables 17

Table 1

Fig.1

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Table 2

Table 3

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Fig.11

Table 4

Table 5

Safe lane-changing time threshold"

模型	$t 1 / s$	$τ l / s$	$t 2 / s$	$τ u / s$
Ⅰ	0.48	0.97	7.20	14.30
Ⅱ	0.72	1.45	10.80	21.45
Ⅲ	0.45	0.91	3.60	7.15
Ⅳ	1.20	2.42	3.60	7.15

Table 5

Fig.12

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类型	算法名称	适用场景
基于图搜索的算法	Dijkstra算法、A^算法、D^算法	全局路径规划
基于曲线插值的算法	圆弧与直线、贝塞尔曲线、多项式曲线、样条曲线、微分平坦曲线等	局部路径规划
基于数值优化的算法	利用目标函数和约束对规划问题进行描述和求解	局部路径规划
基于人工势场的算法	人工势场法	局部路径规划
基于采样的算法	RRT算法、PRM算法和APF算法等	全局路径规划
基于机器学习的算法	模糊逻辑、神经网络、遗传算法和贝叶斯网络等	全局、局部路径规划

车速/（km·h^-1）	横摆角速度阈值/（（º）·s^-1）
车速/（km·h^-1）	μ = 0.4	μ = 0.6	μ = 0.8	μ = 1.0
120	5.880 0	8.820 0	11.760 0	14.700 0
130	5.427 7	8.141 5	10.855 4	13.569 2
140	5.040 0	7.560 0	10.080 0	12.600 0
150	4.704 0	7.056 0	9.408 0	11.760 0
160	4.410 0	6.615 0	8.820 0	11.025 0
170	5.880 0	8.820 0	11.760 0	14.700 0
180	5.427 7	8.141 5	10.855 4	13.569 2

v_x /（km·h^-1）	τ_min/s
v_x /（km·h^-1）	μ = 0.4	μ = 0.6	μ = 0.8	μ = 1.0
120	2.59	2.14	1.89	1.72
130	2.63	2.18	1.93	1.75
140	2.66	2.23	1.97	1.79
150	2.68	2.26	2.00	1.81
160	2.74	2.30	2.04	1.85
170	2.84	2.40	2.13	1.93
180	2.88	2.43	2.16	1.96

模型	v_SV/（km·h^-1）	v_FT/（km·h^-1）	v_PC/（km·h^-1）	D₁/m	D₂/m
Ⅰ	140	160	120	40	40
Ⅱ	140	160	120	60	60
Ⅲ	140	180	100	40	40
Ⅳ	140	160	120	100	20

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