Grouping-alternation-based cooperative merging strategy for connected and automated vehicles

doi:10.3969/j.issn.1674-8484.2022.03.009

Abstract

Abstract:

A grouping-alternation-based cooperative merging strategy was proposed to improve the efficiency of vehicle merging and reduce fuel consumption at highway on-ramps. The main challenge of cooperative merging is to improve traffic performance while ensuring that the computational load is small enough. Therefore, a passing order adjustment algorithm was designed to optimize the passing order of vehicles and simplify the complexity of cooperative merging. Combined with the characteristics of connected and automated vehicles and cooperative merging, the movement rules of vehicles were reconstructed to ensure driving safety and avoid stop-and-go. The results show that the throughput of the new strategy increases by 3.19% and 27.34%, the average delay decreases by 18.60% and 76.57%, and the total fuel consumption reduces by 35.48% and 14.41%, respectively, compared with the no-control and first-in-first-out strategies. The new strategy can be applied to different traffic flow scenarios to meet the real-time requirements of the traffic system and improve the traffic efficiency of highway on-ramps and reduce fuel consumption.

Key words: connected and automated vehicles, cooperative merging, grouping-alternation, highway on-ramp, passing order

CLC Number:

U495

GUAN Xiaokui, HU Maobin. Grouping-alternation-based cooperative merging strategy for connected and automated vehicles[J]. Journal of Automotive Safety and Energy, 2022, 13(3): 482-488.

Figures/Tables 9

References 21

[1]	XU Huile, FENG Shuo, ZHANG Yi, et al. A grouping-based cooperative driving strategy for CAVs merging problems[J]. IEEE Trans Vehi Tech, 2019, 68(6): 6125-6136.
[2]	Chen Na, AREM B V, Alkim T, et al. A hierarchical model-based optimization control approach for cooperative merging by connected automated vehicles[J]. IEEE Trans Intel Transport Syst, 2021, 22(12): 7712-7725. doi: 10.1109/TITS.2020.3007647 URL
[3]	罗孝羚, 蒋阳升. 智能网联车环境下高速匝道汇入车流轨迹优化模型[J]. 交通运输系统工程与信息, 2019, 19(4): 94-100.
	LUO Xiaoling, JIANG Yangsheng. Vehicle trajectory optimization model for ramp based on connect automatically vehicles[J] J Transport Syst Engi Info Tech, 2019, 19(4): 94-100. (in Chinese)
[4]	Rios-Torres J malikopoulos A A. A survey on the coordination of connected and automated vehicles at intersections and merging at highway on-ramps[J]. IEEE Trans Intel Transport Syst, 2017, 18(5): 1066-1077. doi: 10.1109/TITS.2016.2600504 URL
[5]	Pasquale C, Papamichail I, Roncoli C, et al. Two-class freeway traffic regulation to reduce congestion and emissions via nonlinear optimal control[J]. Transport Res Part C-Emerg Tech, 2015, 55: 85-99. doi: 10.1016/j.trc.2015.01.013 URL
[6]	Agarwal S, Kachroo P, Contreras S, et al. Feedback-coordinated ramp control of consecutive on-ramps using distributed modeling and godunov-based satisfiable allocation[J]. IEEE Trans Intel Transport Syst, 2015, 16(5): 2384-2392. doi: 10.1109/TITS.2015.2398453 URL
[7]	华雪东, 王炜, 王昊. 考虑自适应巡航车辆影响的上匝道系统混合交通流模型[J]. 物理学报, 2016, 65(8): 084503.
	HUA Xuedong, WANG Wei, WANG Hao. A hybrid traffic flow model with considering the influence of adaptive cruise control vehicles and on-ramps[J]. Acta Phys Sini, 2016, 65(8): 084503. (in Chinese)
[8]	李克强, 戴一凡, 李升波, 等. 智能网联汽车(ICV)技术的发展现状及趋势[J]. 汽车安全与节能学报, 2017, 8(1): 1-14.
	LI Keqiang, DAI Yifan, LI Shengbo, et al. State-of-theart and technical trends of intelligent and connected vehicles[J]. J Autom Safe Energ, 2017, 8(1): 1-14. (in Chinese)
[9]	HU Zhanyi, HUANG Jin, YANG Zeyu, et al. Embedding robust constraint-following control in cooperative on-ramp merging[J]. IEEE Trans Vehi Tech, 2021, 70(1): 133-145.
[10]	MENG Yue, LI Li, WANG Feiyue, et al. Analysis of cooperative driving strategies for nonsignalized intersections[J]. IEEE Trans Vehi Tech, 2018, 67(4): 2900-2911.
[11]	Dresner K, Stone P. A multiagent approach to autonomous intersection management[J]. J Artif Intel Res, 2008, 31: 591-656.
[12]	Levin M W, Fritz H, Boyles S D. On optimizing reservation-based intersection controls[J]. IEEE Trans Intel Transport Syst, 2017, 18(3): 505-515. doi: 10.1109/TITS.2016.2574948 URL
[13]	DING Jishiyu, LI Li, PENG H, et al. A rule-based cooperative merging strategy for connected and automated vehicles[J]. IEEE Trans Intel Transport Syst, 2020, 21(8): 3436-3446. doi: 10.1109/TITS.2019.2928969 URL
[14]	XU Huile, ZHANG Yi, Cassandras C G, et al. A bi-level cooperative driving strategy allowing lane changes[J]. Transport Res Part C: Emerg Tech, 2020, 120: 102773. doi: 10.1016/j.trc.2020.102773 URL
[15]	LI Li, WANG Feiyue. Cooperative driving at blind crossings using intervehicle communication[J]. IEEE Trans Vehi Tech, 2006, 55(6): 1712-1724.
[16]	XU Huile, ZHANG Yi, LI Li, et al. Cooperative driving at unsignalized intersections using tree search[J]. IEEE Trans Intel Transport Syst, 2020, 21(11): 4563-4571 doi: 10.1109/TITS.2019.2940641 URL
[17]	PEI Huaxin, FENG Shuo, ZHANG Yi, et al. A cooperative driving strategy for merging at on-ramps based on dynamic programming[J]. IEEE Trans Vehi Tech, 2019, 68(12): 11646-11656.
[18]	Nagel K, Schreckenberg M. A cellular automaton model for freeway traffic[J]. J de Phy I, 1992, 2(12): 2221-2229.
[19]	Ahn K, Rakha H, Trani A, et al. Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels[J]. J Transport Engi, 2002, 128(2): 182-190.
[20]	YAO Zhihong, WANG Yi, LIU Bo, et al. Fuel consumption and transportation emissions evaluation of mixed traffic flow with connected automated vehicles and human-driven vehicles on expressway[J]. Energy, 2021, 230: 120766. doi: 10.1016/j.energy.2021.120766 URL
[21]	ZHANG Xuan, JIA Bin, JIANG Rui. Impact of safety assistance driving systems on oscillation magnitude, fuel consumption and emission in a car platoon[J]. Physica A: Statist Mech its Appl, 2018, 505: 995-1007. doi: 10.1016/j.physa.2018.04.033 URL

i	K_i,j
i	j = 0	j = 1	j = 2	j = 3
0	-7.735	0.229 5	-5.610×10^-3	9.773×10^-5
1	0.027 99	0.006 8	-7.722×10^-4	8.380×10^-6
2	-2.228×10^-4	-4.402×10^-5	7.900×10^-7	8.170×10^-7
3	1.09×10^-6	4.80×10^-8	3.27×10^-8	-7.79×10^-9
i	K’_i,j
i	j = 0	j = 1	j = 2	j = 3
0	-7.735	-0.017 99	-4.270×10^-3	1.882 9×10^-4
1	0.028 04	7.720×10^-3	8.375×10^-4	3.387×10^-5
2	-2.199×10^-4	-5.219×10^-5	-7.440×10^-6	2.770×10^-7
3	1.08×10^-6	2.47×10^-7	4.87×10^-8	3.79×10^-10

i	K_i,j
i	j = 0	j = 1	j = 2	j = 3
0	-7.735	0.229 5	-5.610×10^-3	9.773×10^-5
1	0.027 99	0.006 8	-7.722×10^-4	8.380×10^-6
2	-2.228×10^-4	-4.402×10^-5	7.900×10^-7	8.170×10^-7
3	1.09×10^-6	4.80×10^-8	3.27×10^-8	-7.79×10^-9
i	K’_i,j
i	j = 0	j = 1	j = 2	j = 3
0	-7.735	-0.017 99	-4.270×10^-3	1.882 9×10^-4
1	0.028 04	7.720×10^-3	8.375×10^-4	3.387×10^-5
2	-2.199×10^-4	-5.219×10^-5	-7.440×10^-6	2.770×10^-7
3	1.08×10^-6	2.47×10^-7	4.87×10^-8	3.79×10^-10

控制区长度,l_c	250 m
合并区长度,l_m	30 m
通行顺序调整最大阈值,d_opt	35 m
相同方向车辆间最小安全间隙,d_safe1	10 m
冲突方向车辆间最小安全间隙,d_safe2	30 m
最大速度,v_max	15 m/s
最小速度,v_min	5 m/s
最大加速度,a_max	3 m/s²
最大减速度,a_min	- 3 m/s²
平均车头时距,Δt	3 s

控制区长度,l_c	250 m
合并区长度,l_m	30 m
通行顺序调整最大阈值,d_opt	35 m
相同方向车辆间最小安全间隙,d_safe1	10 m
冲突方向车辆间最小安全间隙,d_safe2	30 m
最大速度,v_max	15 m/s
最小速度,v_min	5 m/s
最大加速度,a_max	3 m/s²
最大减速度,a_min	- 3 m/s²
平均车头时距,Δt	3 s

Δt / s	策略	吞吐量辆	平均延迟 s	总油耗 L	模拟时间 ms
Δt_m = 6 Δt_r = 6	无控制	178.96	20.81	21.73	24.87
	先进先出	145.02	72.29	16.38	46.41
	分组交替	184.67	16.94	14.02	78.92
Δt_m = 6 Δt_r = 12	无控制	144.60	1.79	10.95	14.91
	先进先出	144.43	2.17	9.93	25.82
	分组交替	144.49	1.80	9.91	47.92
Δt_m = 12 Δt_r = 6	无控制	144.34	2.50	11.69	16.56
	先进先出	144.42	2.03	9.91	25.50
	分组交替	144.53	1.75	9.90	47.56