考虑前车运动不确定性的多目标自适应巡航控制

doi:10.19562/j.chinasae.qcgc.2023.03.003

Abstract

Abstract:

Considering the performance degradation caused by the uncontrollable movement of the preceding vehicle， this paper proposes a stochastic model predictive control strategy based on the Gaussian process for adaptive cruise control. Firstly， an integration model of the car-following system is constructed based on the kinematic relationship between the vehicles. And objective functions and performance constraints of the car-following system are formulated considering comprehensively the multi-dimensional demand of vehicle security， fuel economy， ride comfort， etc. Then， the radial basis function kernel is introduced to describe the relationship among samples and hyperparameters are obtained via the maximum-likelihood method. Based on historical traffic data， the trajectory of the preceding vehicle is predicted in a short term. Subsequently， in consideration of the error between prediction results and its actual values， probability constraints are introduced to establish the stochastic predictive control model under uncertain environment to ensure the optimal overall performance of the system in the presence of stochastic disturbance. Finally， the superiority and effectiveness of the algorithm are verified by typical scenarios such as cut-in， acceleration for car following， and deceleration for collision avoidance. The results show that the proposed strategy possesses good adaptability to working conditions， which can quickly eliminate the tracking errors and keep consistent with the movement of the preceding vehicle. Thus， it makes the vehicle respond more quickly to the highly dynamic traffic environment.

Key words: adaptive cruise control, stochastic model predictive control, autonomous vehicle, Gaussian process, movement of preceding vehicle

Ziwei Zhang,Ling Zheng,Yinong Li,Xuqiang Qiao,Hao Zheng,Kan Wang. A Multi-objective Adaptive Cruise Control Strategy for Autonomous Vehicle Considering Uncertain Movements of Preceding Vehicle[J].Automotive Engineering, 2023, 45(3): 361-371.

Figures/Tables 21

References 23

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参数	数值	参数	数值
t_h/s	1.5	（a_fmin， a_fmax）	（-2.5， 2.5）
d_s/m	5	（υ_Δd_min， υ_Δd_max）	（-3， 3）
K_L	1.0	（υ_Δv_min， υ_Δv_max）	（-1， 1）
T_L	0.4	（υ_afmin， υ_afmax）	（-0.1， 0.1）
t_TTC/s	-3	（υ_afdesmin， υ_afdesmax）	（-0.1， 0.1）
d_s0/m	5	（Δd_min， Δd_max）	（-5， 5）
T_s/s	0.05	（Δv_min， Δv_max）	（-3， 3）
p	20	（a_fdesmin， a_fdesmax）	（-6， 6）
α	0.95

控制器	前车运动状态预测	预测结果不确定性
MPC	×	×
MPC+GP	√	×
SMPC+GP	√	√

控制器	max（Δd）/m	rms（Δd）/m	max（Δv）/（km·h^-1）	rms（Δv）/（km·h^-1）	max（a）/（m·s^-2）	rms（a）/（m·s^-2）
MPC	3.67	1.41	17.42	6.48	2.27	0.97
MPC+GP	1.83（↓50.14%）	0.79（↓43.97%）	15.79（↓9.36%）	5.91（↓8.80%）	2.31（↑1.76%）	1.00（↑3.09%）
SMPC+GP	2.18（↓40.60%）	0.80（↓43.26%）	12.52（↓28.13%）	5.02（↓22.53%）	2.52（↑11.01%）	0.98（↑1.03%）

控制器	max（Δd）/m	rms（Δd）/m	max（Δv）/（km·h^-1）	rms（Δv）/（km·h^-1）	max（a）/（m·s^-2）	rms（a）/（m·s^-2）
MPC	4.19	0.96	11.75	4.64	2.41	0.94
MPC+GP	4.19（-0%）	0.95（↓1.04%）	11.31（↓3.74%）	4.54（↓2.16%）	2.41（-0%）	0.92（↓2.13%）
SMPC+GP	4.17（↓0.48%）	0.80（↓16.67%）	10.00（↓14.89%）	4.01（↓13.58%）	4.32（↑79.25%）	0.99（↑5.32%）

控制器	max（Δd）/m	rms（Δd）/m	max（Δv）/（km·h^-1）	rms（Δv）/（km·h^-1）	max（a）/（m·s^-2）	rms（a）/（m·s^-2）
MPC	0.99	0.51	9.91	3.28	1.66	0.56
MPC+GP	0.77（↓22.22%）	0.46（↓9.80%）	9.45（↓4.64%）	3.14（↓4.27%）	1.63（↓1.81%）	0.54（↓4.03%）
SMPC+GP	0.84（↓15.15%）	0.22（↓56.86%）	8.01（↓19.17%）	2.71（↓17.38%）	1.74（↑4.82%）	0.54（↓2.61%）

A Multi-objective Adaptive Cruise Control Strategy for Autonomous Vehicle Considering Uncertain Movements of Preceding Vehicle

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