合用相位信控路口生态驾驶轨迹优化模型

辛琪; 王嘉琪; 付锐; 徐猛; 周海洋; 潘应久

doi:10.19818/j.cnki.1671-1637.2025.03.023

合用相位信控路口生态驾驶轨迹优化模型

doi: 10.19818/j.cnki.1671-1637.2025.03.023

辛琪¹,
王嘉琪¹,
付锐¹,
徐猛²,
周海洋¹,
潘应久^1, ,

1.
长安大学汽车学院，陕西西安 710064
2.
北京交通大学系统科学学院，北京 100044

基金项目:

国家自然科学基金项目 52002035

国家自然科学基金项目 52402417

陕西省重点研发计划 2024CY2-GJHX-87

陕西省自然科学基础研究计划项目 2025JC-YBMS-395

中央高校基本科研业务费专项资金项目 300102223205

详细信息

作者简介:
辛琪(1987-)，男，陕西咸阳人，长安大学副教授，博士研究生导师，工学博士，从事智能车辆环境感知系统、运动规划系统、控制系统研究

通讯作者:
潘应久(1990-)，男，山东章丘人，长安大学讲师，工学博士

中图分类号: U491.2
计量
- 文章访问数: 494
- HTML全文浏览量: 75
- PDF下载量: 18
- 被引次数: 0
出版历程
- 收稿日期: 2024-07-09
- 录用日期: 2025-04-02
- 修回日期: 2025-03-06
- 刊出日期: 2025-06-28

Eco-driving trajectory optimization model at signalized intersection considering shared phase

1.
School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China
2.
School of Systems Science, Beijing Jiaotong University, Beijing 100044, China

Funds:

National Natural Science Foundation of China 52002035

National Natural Science Foundation of China 52402417

Key R&D Program in Shaanxi Province of China 2024CY2-GJHX-87

Natural Science Basic Research Plan in Shaanxi Province of China 2025JC-YBMS-395

Fundamental Research Funds for the Central Universities 300102223205

More Information

Corresponding author: PAN Ying-jiu (1990-), male, lecturer, PhD, panyingjiu@chd.edu.cn

Article Text (Baidu Translation)

摘要

摘要: 提出了一种动态规划-交叉口冲突管理策略，在合用相位条件下优化智能网联车辆接近信号交叉口的轨迹，并缓解交叉口内部冲突；基于车辆状态信息、信号相位及配时信息建立动态规划模型，对进口道车辆进行轨迹优化，最大限度利用绿灯时间并减少车辆等待时间；针对存在车流冲突的合用信号相位场景，设计了交叉口冲突管理策略，该策略通过车辆虚映射建立冲突车辆在交叉口通行次序，通过智能驾驶人模型创建安全间距，保证交叉口内交通顺畅通行，并对西安永庆路与永隆路信号交叉口进行了仿真分析。结果表明：与左转保护相位、合用相位情形下的动态规划模型相比，所提模型控制下平均速度分别提高约12.88%、4.14%，百公里能耗分别降低约9.79%、3.97%；与渗透率为0的情形相比，所提模型在渗透率为20%~100%情形下，整体百公里能耗减少约3.56%~13.97%；所提模型控制下的碰撞时间与后侵入时间分析表明，安全性得到显著改善；在交通需求和信号周期波动条件下，所提模型均可实现车辆从进口道至驶离交叉口全过程轨迹优化。
- 智能网联车辆 /
- 信控路口 /
- 虚映射 /
- 轨迹优化 /
- 交叉口冲突 /
- 合用相位
Abstract: A dynamic programming-intersection conflict management strategy was proposed to optimize the trajectories of intelligent connected vehicles approaching signalized intersections under shared phase conditions and mitigate conflicts at intersections. A dynamic programming model was established based on the information of vehicle state and signal phase and timing to optimize the trajectories of vehicles upstream of the signalized intersection, maximize throughput during green time, and reduce waiting time. Besides, an intersection conflict management strategy was designed for the shared phase scenario with traffic conflicts. The strategy determined the sequence of conflicting vehicles passing through the intersection by virtual vehicle mapping and created a safe gap by an intelligent driver model, ensuring smooth traffic flow within the intersection. Finally, a simulation analysis was conducted on the signalized intersection of Yongqing Road and Yonglong Road in Xi'an City. Simulation results show that in contrast to left-turn protected phase and shared phase scenarios under the control of the dynamic programming model, the proposed model improves average speed by 12.88% and 4.14% and reduces energy consumption per 100 km by 9.79% and 3.97%, respectively. Compared to the scenario with 0% penetration rate, the total energy consumption per 100 km under the proposed model decreases by 3.56%-13.97% in scenarios with penetration rates ranging from 20% to 100%. Analysis of the time to collision and post-encroachment time under the proposed model shows a significant improvement in safety. Furthermore, under conditions of varying traffic demands and fluctuating signal cycles, the proposed model can achieve trajectory optimization for vehicles throughout the entire process from entering the lane to leaving the intersection.
- intelligent connected vehicle /
- signalized intersection /
- virtual mapping /
- trajectory optimization /
- intersection conflict /
- shared phase

HTML全文

图 1 交叉口上游车辆速度引导

Figure 1. Vehicular speed guidance at upstream of intersection

下载: 全尺寸图片幻灯片

图 2 不同交通信号模型下车辆轨迹

Figure 2. Vehicular trajectories under different traffic signal models

下载: 全尺寸图片幻灯片

图 3 基于DP-ICMS的信号交叉口车辆轨迹优化流程

Figure 3. Optimization flow of vehicular trajectories at signalized intersections based on DP-ICMS

下载: 全尺寸图片幻灯片

图 4 基于DP的ICV绿灯优化速度引导

Figure 4. Green light optimized speed advisory of ICVs based on DP

下载: 全尺寸图片幻灯片

图 5 基于虚映射和IDM模型的ICMS流程

Figure 5. ICMS process based on virtual mapping and IDM model

下载: 全尺寸图片幻灯片

图 6 流量数据采集路段(单位: pcu·h^-1)

Figure 6. Flow data collection section (unit: pcu·h^-1)

下载: 全尺寸图片幻灯片

图 7 信号交叉口场景

Figure 7. Signalized intersection scenario

下载: 全尺寸图片幻灯片

图 8 不同场景交通信号设置

Figure 8. Traffic signal settings for different scenarios

下载: 全尺寸图片幻灯片

图 9 不同模型下平均等待时间对比

Figure 9. Comparisons of average waiting time under different models

下载: 全尺寸图片幻灯片

图 10 不同模型下车辆速度对比

Figure 10. Comparisons of vehicular speed under different models

下载: 全尺寸图片幻灯片

图 11 不同模型下车辆百公里能耗对比

Figure 11. Comparisons of vehicular energy consumption of per 100 km under different models

下载: 全尺寸图片幻灯片

图 12 不同模型下车辆安全性分布直方图

Figure 12. Histogram of vehicular safety distribution under different models

下载: 全尺寸图片幻灯片

图 13 混行条件下ICV控制场景

Figure 13. ICV control scenarios under mixed condition

下载: 全尺寸图片幻灯片

图 14 混行条件下车辆安全性分布直方图

Figure 14. Histogram of vehicular safety distribution under mixed condition

下载: 全尺寸图片幻灯片

图 15 不同交通需求下的智能网联车辆时空轨迹

Figure 15. ICV spatial-temporal trajectories under different traffic demands

下载: 全尺寸图片幻灯片

图 16 不同信号周期下指标变化

Figure 16. Indicator changes under different traffic signal cycles

下载: 全尺寸图片幻灯片

图 17 不同信号周期下车辆安全性分布直方图

Figure 17. Histogram of vehicular safety distribution under traffic signal cycles

下载: 全尺寸图片幻灯片

表 1 DP-ICMS模型下不同转向车辆控制模式

Table 1. Control modes for vehicles with different turning direction under DP-ICMS models

车辆流向	所在车道直行信号状态	右转车辆有无实际直行前车	控制模式
右转	红灯	无	直接通行
	红灯	有	应与冲突直行前车保持安全距离
	绿灯	-	直接通行
外侧车道直行	红灯	-	GLOSA+与对向冲突左转前车、右转前车保持安全距离
	绿灯	无	GLOSA+与对向冲突左转前车、右转前车保持安全距离
	绿灯	有	GLOSA+与对向冲突左转前车保持安全距离
中间车道直行	-	-	GLOSA+与冲突左转前车保持安全距离
左转车辆	-	-	GLOSA+与对向双车道直行冲突前车保持安全距离

下载: 导出CSV

表 2 参数设置

Table 2. Parameter setting

参数	含义	取值
w₁	速度松弛量权重	1
w₂	位置松弛量权重	1
w₃	末速度与匀速段期望速度的速度波动权重	0.2
w₄	初速度与匀速段期望速度的速度波动权重	0.2
a_max	轨迹优化最大加速度/(m·s^-2)	2.6
a_min	轨迹优化最小加速度/(m·s^-2)	-4.5
t_x	每辆车排队消散时间/s	1.5
v_IDM	IDM模型下的期望速度/(m·s^-1)	18
v_max	道路限速/(m·s^-1)	18
d₀	最小停车间距/m	2.5
T_s	车头时距/s	1.5
T_c	信号周期/s	99

下载: 导出CSV

表 3 不同模型仿真结果

Table 3. Simulation results under different models

模型	整体平均速度/(m·s^-1)	整体平均百公里能耗/(kW·h)	t_TTC < 2 s占比/%	t_PET < 1.5 s占比/%
场景1-IDM	12.81	14.89	56.65	88.64
场景2-IDM	10.28	16.53	18.49	0.00
场景1-DP	11.36	13.34	45.28	87.56
场景2-DP	10.48	14.20	5.29	0.00
DP-ICMS	11.83	12.81	2.64	9.10

下载: 导出CSV

表 4 混行条件下仿真结果

Table 4. Simulation results under mixed condition

ICV渗透率/%	整体平均速度/(m·s^-1)	整体平均百公里能耗/(kW·h)	t_TTC < 2 s占比/%	t_PET < 1.5 s占比/%
0	12.81	14.89	56.65	88.64
20	12.70	14.36	37.90	89.03
40	12.47	13.86	32.31	69.68
60	12.22	13.56	23.03	49.03
80	11.97	13.14	13.24	27.74
100	11.83	12.81	2.64	9.10

下载: 导出CSV

表 5 不同交通需求仿真结果

Table 5. Simulation results under different traffic demands

交通需求/(pcu·h^-1)	模型	平均车头时距/s	平均速度/(m·s^-1)
左转150、直行300	场景1-IDM	3.26	9.75
左转150、直行300	DP-ICMS	2.67	11.40
左转150、直行350	场景1-IDM	2.96	9.28
左转150、直行350	DP-ICMS	2.43	11.30
左转150、直行400	场景1-IDM	2.80	8.66
左转150、直行400	DP-ICMS	2.40	11.62
直行350、左转100	场景1-IDM	2.86	7.46
直行350、左转100	DP-ICMS	2.56	10.25
直行350、左转150	场景1-IDM	3.28	6.84
直行350、左转150	DP-ICMS	2.84	10.72
直行350、左转200	场景1-IDM	2.87	5.20
直行350、左转200	DP-ICMS	2.42	10.50

下载: 导出CSV

表 6 不同信号周期仿真结果

Table 6. Simulation results under different traffic signal cycles

信号周期/s	平均速度/(m·s^-1)	整体平均百公里能耗/(kW·h)	t_TTC < 2 s占比/%	t_PET < 1.5 s占比/%
79	12.24	12.55	1.72	12.88
99	11.83	12.81	2.64	9.10
119	11.43	12.98	2.25	9.85

下载: 导出CSV

参考文献(30)

[1]	付锐, 张雅丽, 袁伟. 生态驾驶研究现状及展望[J]. 中国公路学报, 2019, 32(3): 1-12. FU Rui, ZHANG Ya-li, YUAN Wei. Progress and prospect in research on eco-driving[J]. China Journal of Highway and Transport, 2019, 32(3): 1-12.
[2]	WANG R M, ZHANG X R, XU Z G, et al. Research on performance and function testing of V2X in a closed test field[J]. Journal of Advanced Transportation, 2021, 2021: 9970978.
[3]	杨澜, 赵祥模, 吴国垣, 等. 智能网联汽车协同生态驾驶策略综述[J]. 交通运输工程学报, 2020, 20(5): 58-72. doi: 10.19818/j.cnki.1671-1637.2020.05.004 YANG Lan, ZHAO Xiang-mo, WU Guo-yuan, et al. Review on connected and automated vehicles based cooperative eco-driving strategies[J]. Journal of Traffic and Transportation Engineering, 2020, 20(5): 58-72. doi: 10.19818/j.cnki.1671-1637.2020.05.004
[4]	刘显贵, 王晖年, 洪经纬, 等. 网联环境下信号交叉口车速控制策略及优化[J]. 交通运输系统工程与信息, 2021, 21(2): 82-90. LIU Xian-gui, WANG Hui-nian, HONG Jing-wei, et al. Speed control strategy and optimization of signalized intersection in network environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2): 82-90.
[5]	刘显贵, 洪经纬, 王晖年, 等. 基于前车速度预测的信号交叉口汽车生态驾驶控制策略[J]. 安全与环境学报, 2021, 21(6): 2743-2750. LIU Xian-gui, HONG Jing-wei, WANG Hui-nian, et al. Vehicle eco-driving control strategy based on speed prediction of the front vehicle at the signalized intersection[J]. Journal of Safety and Environment, 2021, 21(6): 2743-2750.
[6]	胡永辉, 金旭峰, 王亦兵, 等. 智能网联混行动力异构交通流生态驾驶[J]. 中国公路学报, 2022, 35(3): 15-27. HU Yong-hui, JIN Xu-feng, WANG Yi-bing, et al. Eco-driving of connected and automated vehicles in mixed and power-heterogeneous traffic flow[J]. China Journal of Highway and Transport, 2022, 35(3): 15-27.
[7]	鹿应荣, 许晓彤, 丁川, 等. 车联网环境下信号交叉口车速控制策略[J]. 交通运输系统工程与信息, 2018, 18(1): 50-58, 95. LU Ying-rong, XU Xiao-tong, DING Chuan, et al. A speed control strategy at signalized intersection under connected vehicle environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(1): 50-58, 95.
[8]	辛琪, 王嘉琪, 杨文科, 等. 信控路段混行交通生态驾驶深度强化学习模型[J]. 交通运输系统工程与信息, 2024, 24(3): 127-139. XIN Qi, WANG Jia-qi, YANG Wen-ke, et al. Eco-driving under mixed autonomy at signalized intersection: A deep reinforcement learning model[J]. Journal of Transportation Systems Engineering and Information Technology, 2024, 24(3): 127-139.
[9]	刘春禹, 刘永红, 罗霞, 等. 混合交通流环境下单交叉口自动驾驶车辆轨迹优化[J]. 交通运输系统工程与信息, 2022, 22(2): 154-162. LIU Chun-yu, LIU Yong-hong, LUO Xia, et al. Trajectory optimization of connected vehicles at isolated intersection in mixed traffic environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 154-162.
[10]	蒋贤才, 徐慧智. 混合驾驶环境下交叉口空间资源动态控制方法[J]. 交通运输系统工程与信息, 2023, 23(6): 63-73. JIANG Xian-cai, XU Hui-zhi. Dynamic control method for intersection space resources in mixed traffic environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(6): 63-73.
[11]	JIANG X C, SHANG Q P. A dynamic CAV-dedicated lane allocation method with the joint optimization of signal timing parameters and smooth trajectory in a mixed traffic environment[J]. IEEE Transactions on Intelligent Transportation Systems, 2023, 24(6): 6436-6449.
[12]	王润民, 张心睿, 赵祥模, 等. 混行环境下网联信号交叉口车路协同控制方法[J]. 交通运输工程学报, 2022, 22(3): 139-151. doi: 10.19818/j.cnki.1671-1637.2022.03.011 WANG Run-min, ZHANG Xin-rui, ZHAO Xiang-mo, et al. Vehicle-infrastructure cooperative control method of connected and signalized intersection in mixed traffic environment[J]. Journal of Traffic and Transportation Engineering, 2022, 22(3): 139-151. doi: 10.19818/j.cnki.1671-1637.2022.03.011
[13]	赵祥模, 张心睿, 王润民, 等. 网联交叉口信号-车辆轨迹协同优化控制方法[J]. 汽车工程, 2021, 43(11): 1577-1586. ZHAO Xiang-mo, ZHANG Xin-rui WANG Run-min, et al. Cooperative optimization control method of traffic signals and vehicle trajectories at connected intersection[J]. Automotive Engineering, 2021, 43(11): 1577-1586.
[14]	ZHAO W M, NGODUY D, SHEPHERD S, et al. A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 802-821.
[15]	吴伟, 秦少敏, 马万经, 等. 考虑自动驾驶专用相位的交叉口信号控制方法[J]. 中国公路学报, 2023, 36(10): 183-196. WU Wei, QIN Shao-min, MA Wan-jing, et al. Intersection signal control method considering exclusive autonomous-vehicle phase[J]. China Journal of Highway and Transport, 2023, 36(10): 183-196.
[16]	杜煜, 上官伟, 柴琳果, 等. 信号交叉口基于出发时刻预测的生态驾驶方法[J]. 中国公路学报, 2022, 35(6): 277-288. DU Yu, SHANGGUAN Wei, CHAI Lin-guo, et al. Eco-driving method for signalized intersection based on departure time prediction[J]. China Journal of Highway and Transport, 2022, 35(6): 277-288.
[17]	ZHOU M F, YU Y, QU X B. Development of an efficient driving strategy for connected and automated vehicles at signalized intersections: a reinforcement learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2020, 21(1): 433-443.
[18]	李捷, 吴晓东, 许敏, 等. 基于强化学习的城市场景多目标生态驾驶策略[J]. 汽车工程, 2023, 45(10): 1791-1802. LI Jie, WU Xiao-dong, XU Min, et al. Reinforcement learning based multi-objective eco-driving strategy in urban scenarios[J]. Automotive Engineering, 2023, 45(10): 1791-1802.
[19]	MOSHARAFIAN S, AFZALI S, MOHAMMADPOUR VELNI J. Leveraging autonomous vehicles in mixed-autonomy traffic networks with reinforcement learning-controlled intersections[J]. Transportation Letters, 2023, 15(9): 1218-1229.
[20]	LI J, FOTOUHI A, PAN W J, et al. Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties[J]. Energy, 2023, 279: 128139.
[21]	姜涵, 张健, 张海燕, 等. 基于强化学习的交叉口智能网联车多目标通行控制方法[J]. 交通信息与安全, 2024, 42(1): 84-93. JIANG Han, ZHANG Jian, ZHANG Hai-yan, et al. A multi- objective traffic control method for connected and automated vehicle at signalized intersection based on reinforcement learning[J]. Journal of Transport Information and Safety, 2024, 42(1): 84-93.
[22]	CHENG Y Q, HU X B, CHEN K M, et al. Online longitudinal trajectory planning for connected and autonomous vehicles in mixed traffic flow with deep reinforcement learning approach[J]. Journal of Intelligent Transportation Systems, 2023, 27(3): 396-410.
[23]	WEGENER M, KOCH L, EISENBARTH M, et al. Automated eco-driving in urban scenarios using deep reinforcement learning[J]. Transportation Research Part C: Emerging Technologies, 2021, 126: 102967.
[24]	陈浩, 庄伟超, 殷国栋, 等. 网联电动汽车信号灯控路口经济性驾驶策略[J]. 东南大学学报(自然科学版), 2021, 51(1): 178-186. CHEN Hao, ZHUANG Wei-chao, YIN Guo-dong, et al. Eco-driving control strategy of connected electric vehicle at signalized intersection[J]. Journal of Southeast University (Natural Science Edition), 2021, 51(1): 178-186.
[25]	王方凯, 杨晓光, 江泽浩, 等. 新型混合交通流场景下交叉口信号控制和轨迹控制协同优化方法[J]. 交通信息与安全, 2024, 42(1): 76-83, 123. WANG Fang-kai, YANG Xiao-guang, JIANG Ze-hao, et al. Joint optimization of intersection signal control and trajectory control in novel heterogenous traffic flow scenarios[J]. Journal of Transport Information and Safety, 2024, 42(1): 76-83, 123.
[26]	YE Q W, CHEN X M, LIAO R H, et al. Development and evaluation of a vehicle platoon guidance strategy at signalized intersections considering fuel savings[J]. Transportation Research Part D: Transport and Environment, 2019, 77: 120-131.
[27]	钱国敏, 凡俊生, 章立辉, 等. 混行交叉口网联自动车专用道布设方案评价模型[J]. 长安大学学报(自然科学版), 2022, 42(2): 115-126. QIAN Guo-min, FAN Jun-sheng, ZHANG Li-hui, et al. Evaluation model for configuration schemes of dedicated connected and autonomous lanes at intersections with heterogeneous traffic flows[J]. Journal of Chang'an University (Natural Science Edition), 2022, 42(2): 115-126.
[28]	吴伟, 刘洋, 刘威, 等. 自动驾驶环境下交叉口车辆路径规划与最优控制模型[J]. 自动化学报, 2020, 46(9): 1971-1985. WU Wei, LIU Yang, LIU Wei, et al. A novel autonomous vehicle trajectory planning and control model for connected-and-autonomous intersections[J]. Acta Automatica Sinica, 2020, 46(9): 1971-1985.
[29]	宗芳, 石蕊, 刘怿轩, 等. 信号交叉口行车风险场建立及车辆通行行为优化[J]. 中国公路学报, 2022, 35(10): 244-253. ZONG Fang, SHI Rui, LIU Yi-xuan, et al. Construction of risk field and optimization of driving behaviors for signalized intersections[J]. China Journal of Highway and Transport, 2022, 35(10): 244-253.
[30]	KURCZVEIL T, LOPEZ P A, SCHNIEDER E. Implementation of an energy model and a charging infrastructure in SUMO[C]// BEHRISCH M, KRAJZEWICZ D, WEBER M. Simulation of Urban Mobility. Berlin: Springer, 2014: 33-43.