Optimal lateral acceleration driver model based on trajectory prediction

doi:10.3969/j.issn.1674-8484.2023.03.009

Abstract

Abstract:

An optimal lateral acceleration driver model was proposed based on the trajectory prediction to improve the active safety of intelligent vehicles. The model was used to predict the vehicle trajectory with an assumption of the vehicle driving at a constant yaw velocity according to the current vehicle attitude; Determine the ideal steering wheel angle by calculating the lateral deviation after the preview time. The path tracking effect of the model was simulated and analyzed for the driving conditions on the double-shift roads and circular roads; The simulation model was verified by using a series of real vehicle tests. The results show that the maximum tracking lateral deviation of the simulation results is less than 10 cm with the deviation of the real vehicle tests being less than 25 cm at the constant speed of 54 km/h. Therefore, the model can stably follow the target path when the vehicle is traveling, verifying the accuracy of the model.

Key words: active safety, intelligent vehicles, trajectory prediction, look-ahead scheme, optimal lateral acceleration, path tracking, closed-loop simulation, field tests

CLC Number:

U461.6

ZHOU Xing, LIU Fuyun, TANG Zhentian, DENG Jucai. Optimal lateral acceleration driver model based on trajectory prediction[J]. Journal of Automotive Safety and Energy, 2023, 14(3): 338-345.

Figures/Tables 18

References 10

[1]	Reddy R, Ellis J. Contribution to the Simulink of driver-vehicle-road system[R]. SAE Paper 810513, 1981.
[2]	郭孔辉. 驾驶员—汽车闭环系统操纵运动的预瞄最优曲率模型[J]. 汽车工程, 1984, 6(3): 1-16.
	GUO Konghui. A preview optimal curvature model for driver-vehicle closed-loop control motion[J]. Automo Engi, 1984, 6(3): 1-16. (in Chinese)
[3]	高振海. 汽车方向预瞄式自适应PD控制算法[J]. 机械工程学报, 2004, 52(5): 101-105.
	GAO Zhenhai. Vehicle orientation preview type adaptive PD control algorithm[J]. J Mech Engin, 2004, 52(5): 101-105. (in Chinese)
[4]	解炬. 基于转向和车速综合控制多目标评价的智能汽车驾驶员模型研究[D]. 江苏: 江苏大学, 2018.
	XIE Ju. Research on Intelligent vehicle driver model based on multi-objective evaluation of steering and vehicle speed integrated control[D]. Jiangsu: Jiangsu University, 2018. (in Chinese)
[5]	马芳武, 佘烁, 吴量, 等. 基于时间系数的单点预瞄驾驶员模型分析[J]. 汽车工程, 2019, 41(3): 340-345.
	MA Fanwu, SHE Shuo, WU Liang, et al. Single point preview driver model analysis based on time coefficient[J]. Automo Engi 2019, 41(3): 340-345. (in Chinese)
[6]	江浩斌, 周新宸, 李傲雪. 智能汽车仿人转向控制驾驶员模型分析[J]. 江苏大学学报(自然科学版), 2021, 42(4): 373-381.
	JIANG Haobing, ZHOU Xinchen, LI Aoxue. Driver model analysis of intelligent vehicle humanoid steering control[J]. J Jiangsu Univ (Nat Sci Ed), 2021, 42(4): 373-381. (in Chinese)
[7]	谢东. 基于速度策略的纵横向驾驶员模型[J]. 汽车实用技术, 2021, 46(12): 49-50.
	XIE Dong. Vertical and horizontal driver model based on speed strategy[J]. Automo Appl Tech, 2021, 46(12): 49-50. (in Chinese)
[8]	曹艳玲, 张琦. 自适应神经模糊推理的方向驾驶员模型研究[J]. 机械设计与制造, 2021, 59(1): 185-187+192.
	CAO Yanling, ZHANG Qi. Adaptive neural fuzzy reasoning for directional driver model[J]. Mach Design Manufa, 2021, 59(1): 185-187+192. (in Chinese)
[9]	叶明, 全洪乾, 刘景升, 等. 考虑道路条件和车速的横纵向控制驾驶员模型[J]. 重庆理工大学学报(自然科学), 2022, 36(10): 41-50.
	YE Ming, QUAN Hongqian, LIU Jingsheng, et al. A driver model for lateral and longitudinal control considering road conditions and vehicle speed[J]. J Chongqing Univ Tech (Nat Sci), 2022, 36(10): 41-50. (in Chinese)
[10]	余志生. 汽车理论, 第5版[M]. 北京: 机械工业出版社, 2009: 144-146.
	YU Zhisheng. Automobile Theory (5th Ed)[M]. Beijing: China Machinery Industry Press, 2009: 144-146. (in Chinese)

整车质量, m	1.412 t
质心至前轴的距离, L_a	1.015 m
质心至后轴的距离, L_b	1.895 m
前轮侧偏刚度, k₁	110 kN/rad
后轮侧偏刚度, k₂	110 kN/rad
前轮到转向柱的角传动比, i	15.5
整车绕铅垂轴转动惯量, I_Z	1.527 t

整车质量, m	1.412 t
质心至前轴的距离, L_a	1.015 m
质心至后轴的距离, L_b	1.895 m
前轮侧偏刚度, k₁	110 kN/rad
后轮侧偏刚度, k₂	110 kN/rad
前轮到转向柱的角传动比, i	15.5
整车绕铅垂轴转动惯量, I_Z	1.527 t

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