Research on Event-Triggered Control of Heterogeneous Cooperative Vehicle Platoons Considering Time Delay

doi:10.12068/j.issn.1005-3026.2024.04.011

Abstract

Abstract:

To address the problem of heterogeneity and time delay caused by mixed traffic， a heterogeneous cooperative event‐triggered control method with time delay for autonomous driving platoons was proposed. Firstly， a heterogeneous vehicle delay model taking into account mixed traffic was established. Secondly， the event‐triggered controller in different modes was designed based on PID（proportional?integral?derivative） control and model predictive control（MPC）. Thirdly， under urban and emergency conditions， the proposed controller was simulated and analyzed. Finally， a real‐car experiment was conducted based on the Jetson Nano model car. The simulation and experiment results illustrated that the proposed event‐triggered control method strikes a better balance between control accuracy and calculation speed under different circumstances. In the emergency situations with communication delay， it can still maintain lower errors.

Key words: autonomous driving, heterogeneous cooperation, event?triggered, time delay, vehicle platoon

CLC Number:

U 495

Chuan-yin TANG, Ji-feng XIA, Ming-li ZHANG, Long-jie WU. Research on Event-Triggered Control of Heterogeneous Cooperative Vehicle Platoons Considering Time Delay[J]. Journal of Northeastern University(Natural Science), 2024, 45(4): 540-547.

Figures/Tables 11

Fig.1 Mobile Internet

Fig.2 Basic V2V structure

Fig.3 Sketch map of vehicle platoons

Table 1 Time delay simulation parameters

参数	值
参与编队的车辆数n	[2,3,4,5,6]
车辆的计算能力最大值c/（Mb·s^-1）	[5,10,15,20,25]
输入数据量m_in/（Mb·s^-1）	0.5
γ	0.1

Fig.4 Time delay simulation under three strategies

Fig.5 Main pseudo code of the event‐triggered algorithm

Fig.6 Framework of the event‐triggered controller algorithm

Table 2 Parameters of vehicle1 motion in urban condition

时间/s	速度/（m·s^-1）	加速度/（m·s^-2）
0~5	20
5~10		1
10~25	25
25~30		-0.5
30~50	22.5
50~55		-1
55~70	17.5
70~75		0.5
75~100	20

Fig.7 State simulation

Fig.8 MPC longitudinal tracking simulation

Fig.9 Field experiment

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