基于云控系统的队列预测性巡航与换道决策

doi:10.19562/j.chinasae.qcgc.2023.08.001

Abstract

Abstract:

The predictive cruise and lane-changing decision method for platoon based on cloud control system is proposed in this paper to improve the safety， economy， efficiency and smoothness of platoon. This method obtains dynamic traffic information through roadside infrastructure and uploads it to the cloud platform， which uses the predictive model to estimate the future state of environmental vehicles. The penalty of different actions of the platoon is reflected in the objective function， by minimizing which the longitudinal acceleration and lateral lane-changing decision sequence are optimized synergistically， with the decision results sent to the vehicle for tracking and control. Sumo and Matlab are used to establish the simulation environment， and five sets of simulation conditions with different traffic flows are designed. The simulation results show that compared to the microscopic driving model （IDM+MOBIL）， the platoon with the proposed method can reduce the collision risk during cruise by 42.2% and the collision risk during lane change by 3.41%， with an average fuel saving rate of 1.22%， an increase of speed by 0.83%， and smoothness by 49.84%， better than the microscopic driving model in safety， economy， efficiency and smoothness.

Key words: cloud control system, platoon, predictive cruise control, lane-changing decision, driving strategies

Run Mei,Duanfeng Chu,Bolin Gao,Keqiang Li,Wei Cong,Chaoyi Chen. Predictive Cruise and Lane-Changing Decision for Platoon Based on Cloud Control System[J].Automotive Engineering, 2023, 45(8): 1299-1308.

Figures/Tables 18

队列车辆数量n	3
队列车辆间距d	18 m
预测时域N_p	30
控制时域N_c	10
采样间隔ΔT	0.5 s
期望速度v_des	20 m/s
安全车头时距 $t s a f e t h w$	1.5 s
安全碰撞时间 $t s a f e t t c$	3 s
常数α	10
常数β	0.5

References 15

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质量m	6.7 t
车辆长度L	7 m
轮胎半径r	0.376 m
迎风面积A	5.1 m²
空气阻力系数C_D	0.65
滚动阻力系数f	0.007 6
发动机转速范围ω_e	749~3 200 r/min
发动机转矩范围T_e	-325~380 N·m
主减速器传动比i₀	4.33
传动比i_g	［5.9，3.365，2.129.1.518，1，0.783］
传动效率η₀	0.915

环境车跟驰模型	IDM^［14］
环境车换道模型	LC2013^［14］
环境车加速度范围a_range	-4.5~2.6 m/s²
左侧车道环境车初始速度v_l0	25 m/s
右侧车道环境车初始速度v_r0	15 m/s

跟车权重ω_f	100
换道权重ω_c	1 500
油耗权重ω_o	5 000
速度权重ω_v	2
加速度权重ω_a	100
车道权重ω_l	1

工况编号	1	2	3	4	5
交通流量/（辆·h^-1）	100	150	200	250	300
队列进入时间/s	54	36	27	21.5	20

状态量	预测结果	实际结果
CPV位置/m	230	229.1
CPV速度/（m·s^-1）	15	14.9
CPV所在车道	0	0
TPV位置/m	380	378.7
TPV速度/（m·s^-1）	25	24.9
TPV所在车道	1	1

Predictive Cruise and Lane-Changing Decision for Platoon Based on Cloud Control System

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Figures/Tables 18

References 15

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工况	算法	巡航		换道
工况	算法	碰撞时间倒数均值/s^-1	降低百分比/%	碰撞时间倒数均值/s^-1	降低百分比/%
工况1	Baseline	0.018 7		0.014 58
工况1	CPPCLC	0.016 6	11.619 1	0.014 64	-0.397 7
工况2	Baseline	0.016 1		0.016 6
工况2	CPPCLC	0.013 4	17.205 2	0.015 8	4.662
工况3	Baseline	0.015 7		0.018 1
工况3	CPPCLC	0.014 3	9.056	0.017 8	1.786 1
工况4	Baseline	0.015 7		0.019 1
工况4	CPPCLC	0.002 8	81.907 9	0.009 7	49.145 1
工况5	Baseline	0.009 1		0.012 8
工况5	CPPCLC	0.000 8	91.207 9	0.017 6	-38.143 6
平均值	Baseline	0.015 1		0.016 2
平均值	CPPCLC	0.009 6	42.20	0.015 1	3.41

工况	算法	百公里油耗/L	节油率/%
工况1	Baseline	11.322 5
工况1	CPPCLC	11.183 6	1.226 5
工况2	Baseline	11.493 6
工况2	CPPCLC	11.358 4	1.175 9
工况3	Baseline	11.866 0
工况3	CPPCLC	11.678 8	1.576 9
工况4	Baseline	12.131 0
工况4	CPPCLC	11.997 5	1.100 5
工况5	Baseline	12.182 1
工况5	CPPCLC	12.058 7	1.012 5
平均值	Baseline	11.799
平均值	CPPCLC	11.655	1.22

工况	算法	平均速度/ （m·s^-1）	速度提升百分比/%
工况1	Baseline	19.460 5
工况1	CPPCLC	19.487 3	0.138 1
工况2	Baseline	19.385 7
工况2	CPPCLC	19.439 4	0.277 4
工况3	Baseline	19.230 9
工况3	CPPCLC	19.231 1	0.001 1
工况4	Baseline	18.944 8
工况4	CPPCLC	19.271 9	1.726 5
工况5	Baseline	18.873 5
工况5	CPPCLC	19.256 2	2.027 4
平均值	Baseline	19.179
平均值	CPPCLC	19.337	0.83

工况	算法	加速度波动/（m²·s^-4）	加速度波动降低百分比/%
工况1	Baseline	0.011 8
工况1	CPPCLC	0.005 1	56.529 1
工况2	Baseline	0.012 4
工况2	CPPCLC	0.006 1	51.094 4
工况3	Baseline	0.015 4
工况3	CPPCLC	0.009 6	37.553 6
工况4	Baseline	0.021 7
工况4	CPPCLC	0.009 7	55.454 3
工况5	Baseline	0.019 5
工况5	CPPCLC	0.01	48.583 8
平均值	Baseline	0.016 2
平均值	CPPCLC	0.008 1	49.87

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