Trajectory planning algorithm of autonomous vehicle based on multi-index coupling

doi:10.3969/j.issn.1674-8484.2023.04.010

Abstract

Abstract:

Urban traffic environment puts forward higher requirements for the safety, comfort, compliance, traffic efficiency and other indicators of autonomous vehicles. A spatiotemporal coupled real-time trajectory planning method was proposed, and the above quantitative indexes were introduced into its evaluation function. The algorithm generated trajectories in a set of driving corridors, selected the optimal trajectories according to a multi-index evaluation function combining horizontal and longitudinal comfort, safety and utility, and tested them in a simulated roundabout. The results show that the acceleration curve is smooth and the maximum value is 0.66 m/s² when comfort is the priority; The generated trajectory is deviated by 0.19 m on average to avoid collision with nearby obstacles when safety comes first; The generated trajectory has a maximum velocity of 9.97 m/s and exhibits a more positive acceleration profile when utility takes precedence. The algorithm can safely avoid dynamic obstacles while maintaining transverse and longitudinal acceleration, and the average cost of each iteration is 47.45 ms, which meets the real-time requirements.

Key words: autonomous driving, trajectory planning, multi-index coupling, evaluation function, real-time

CLC Number:

U461.91

HUANG Pengcheng, PEI Xiaofei, ZHOU Honglong, CHEN Ci. Trajectory planning algorithm of autonomous vehicle based on multi-index coupling[J]. Journal of Automotive Safety and Energy, 2023, 14(4): 480-487.

Figures/Tables 15

References 20

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决策变量	轨迹性能指标	公式
纵向舒适性	纵向加速度平均值	$\overline{a_x(s)}$
纵向舒适性	纵向加加速度平均值	$\overline{j_x(s)}$
横向舒适性	横向加速度平均值	$\overline{a_y(s)}$
横向舒适性	横向加加速度平均值	$\overline{j_y(s)}$
安全性	车道偏离距离	$\int_{s_0}^{s_f}\mathrm{d}_i(s)\mathrm{d}s$
效用性	正向纵向加速度	$\overline{\max{(a_x(s),0)}}$
效用性	平均速度	$\overline{v_s}$

决策变量	轨迹性能指标	公式
纵向舒适性	纵向加速度平均值	$\overline{a_x(s)}$
纵向舒适性	纵向加加速度平均值	$\overline{j_x(s)}$
横向舒适性	横向加速度平均值	$\overline{a_y(s)}$
横向舒适性	横向加加速度平均值	$\overline{j_y(s)}$
安全性	车道偏离距离	$\int_{s_0}^{s_f}\mathrm{d}_i(s)\mathrm{d}s$
效用性	正向纵向加速度	$\overline{\max{(a_x(s),0)}}$
效用性	平均速度	$\overline{v_s}$

决策变量	实验1	实验2	实验3	实验4	实验5
纵向舒适性	1.0	1.0	1.0	1.0	0.5
横向舒适性	0.1	1.0	1.0	1.0	0.5
安全性	0.1	0.1	1.0	1.0	0.5
效用性	0.1	0.1	0.1	1.0	0.5

决策变量	实验1	实验2	实验3	实验4	实验5
纵向舒适性	1.0	1.0	1.0	1.0	0.5
横向舒适性	0.1	1.0	1.0	1.0	0.5
安全性	0.1	0.1	1.0	1.0	0.5
效用性	0.1	0.1	0.1	1.0	0.5

最大横向加速度，a_max,_y	3.0 m/s²
最大纵向加速度，a_max,_x	3.0 m/s²
最小纵向加速度，a_min,_x	-5.0 m/s²
最大限制速度，v_limit	15.0 m/s
最小跟车距离，d_c	10.0 m
安全名义距离，d_o	45.0 m
最大制动加速度，a_max,dec	-5.0 m/s²
最大速度，v_max	15.0 m/s