Speed Trajectory Optimization of Connected Autonomous Vehicles at Signalized Intersections
-
摘要: 以网联自动驾驶汽车(Connected Autonomous Vehicle, CAV)为研究对象, 研究了CAV车队通过城市信号交叉口的速度轨迹优化控制策略。基于最优控制理论, 采用CAV的自动驾驶模型描述车间相互作用, 以所有CAV车辆在行驶过程中的总油耗为优化目标, 根据信号灯的配时信息建立模型约束, 通过优化CAV头车的速度轨迹, 保证整个CAV车队在绿灯相位下快速通过交叉口并实现油耗最小。为了对该优化控制进行高效求解, 采用离散Pontryagin极小值原理建立最优解的必要条件, 利用基于神经网络训练的弹性反向传播(Resilient backpropagation, RPROP)算法设计了数值求解算法。多个典型场景的仿真结果显示: 整个CAV车队均能在不停车的情形下通过信号交叉口, 避免因在红灯时间窗到达停车线造成的停车、启动等过程, 总油耗量最高可减少69.74%。该控制方法利用网联自动驾驶技术的优势, 显著改善了城市交通通行效率和燃油经济性。Abstract: The work studies the optimal control strategy of the speed trajectory for the connected autonomous vehicle(CAV)platoon at urban signalized intersections. Based on the optimal control theory, the automatic driving model is utilized to describe the interaction among vehicles. With the total fuel consumption for the CAV platoon considered as the optimization objective, the constraints of the model are established according to the timing phase of the traffic signal. All the CAVs in the platoon can pass through the intersection with the minimized total fuel consumption by optimizing the speed trajectory for the leading CAV. The necessary conditions for the optimal solution are obtained based on Pontryagin's minimum principle to solve the proposed optimal controller. Then, the numerical solving algorithm is developed utilizing the resilient backpropagation(RPROP)solution algorithm. The simulation results for multiple representative scenarios show that the whole CAV platoon can pass through the signalized intersection without any stop and avoid stopping and starting caused by reaching the stop line at the red time window. Moreover, the total amount of fuel consumption can be decreased by 69.74% at most. The proposed method which takes advantage of CAV technology can improve the traffic efficiency and fuel economy for urban transportation.
-
表 1 CAV车辆的总油耗量
Table 1. Overall fuel consumption of the CAVs
场景 优化控制前油耗量/g 优化控制后油耗量/g 燃油经济性改善幅度/% 1 20 863 6 313 69.74 2 52 859 24 726 53.22 
下载: 导出CSV
-
[1] 康彦龙, 袁勃. 科技部部长万钢: 智能互联是未来汽车发展趋势[N/OL]. (2017-03-01)[2021-01-10]. http://politics.people.com.cn/n1/2017/0331/c1001-29183311.htmlKANG Yanlong, YUAN Bo. WAN Gang the Minister of science and technology: Intelligent interconnection is the trend of automobile development[N/OL]. (2017-03-01) [2021-01-10]. http://politics.people.com.cn/n1/2017/0331/c1001-29183311.html (in Ch-inese) [2] 《中国公路学报》编辑部. 中国汽车工程学术研究综述·2017[J]. 中国公路学报, 2017, 30(6): 1-197. doi: 10.3969/j.issn.1001-7372.2017.06.001The Editorial Office of China Journal of Highway and Transport. Summary of academic research on automotive engineering in China 2017[J]. China Journal of Highway and Transport, 2017, 30(6): 1-197. (in Chinese) doi: 10.3969/j.issn.1001-7372.2017.06.001 [3] 蔡翠. 我国智慧交通发展的现状分析与建议[J]. 公路交通科技(应用技术), 2013, 9(6): 224-227. https://www.cnki.com.cn/Article/CJFDTOTAL-GLJJ201306063.htmCAI Cui. Analysis and suggestions on the development of intelligent transportation in China[J]. Journal of Highway and Transportation Research and Development(Application Technology), 2013, 9(6): 224-227. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-GLJJ201306063.htm [4] 蒋贤才, 张龙洋, 高苏. 考虑车速诱导及车辆到达特征的交叉口信号优化控制方法[J]. 大连交通大学学报, 2019, 40(6): 1-8. https://www.cnki.com.cn/Article/CJFDTOTAL-DLTD201906001.htmJIANG Xiancai, ZHANG Longyang, GAO Su. Intersection signal optimization control method considering speed guidance and vehicle arrival characteristics[J]. Journal of Dalian Jiaotong University, 2019, 40(6): 1-8. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLTD201906001.htm [5] LI Zhuofei, ELEFTERIADOU L, RANKA S. Signal control optimization for automated vehicles at isolated signalized intersections[J]. Transportation Research Part C: Emerging Technologies, 2014(49): 1-18. http://www.onacademic.com/detail/journal_1000036798296910_d1e1.html [6] YAO Handong, CUI Jianxun, LI Xiaopeng, et al. A trajectory smoothing method at signalized intersection based on individualized variable speed limits with location optimization[J]. Transportation Research Part D: Transport and Environment, 2018(62): 456-473. http://www.onacademic.com/detail/journal_1000040271695210_7b2f.html [7] SOLEIMANIAMIRI S, GHIASI A, LI X P, et al. An analytical optimization approach to the joint trajectory and signal optimization problem for connected automated vehicles[OL]. (2020-11)[2021-08-07]. https://www.sciencedirect.com/science/article/pii/S0968090X20306719 [8] 鹿应荣, 许晓彤, 丁川, 等. 连续信号交叉口网联自动驾驶车速控制[J]. 北京航空航天大学学报, 2018, 44(11): 2257-2266. https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK201811002.htmLU Yingrong, XU Xiaotong, DING Chuan, et al. Automatic driving speed control of continuous signalized intersection[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(11): 2257-2266. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK201811002.htm [9] HE Xiaozheng, LIU Henry, LIU Xiaobo. Optimal vehicle speed trajectory on a signalized arterial with consideration of queue[J]. Transportation Research Part C: Emerging Technologies, 2015(61): 106-120. http://www.sciencedirect.com/science?_ob=ShoppingCartURL&_method=add&_eid=1-s2.0-S0968090X15003800&originContentFamily=serial&_origin=article&_ts=1487275891&md5=0a30d2642ec2becaf13919dd1d41b7c0 [10] JIANG Huifu, HU Jia, AN Shi, et al. Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment[J]. Transportation Research Part C: Emerging Technologies, 2017(79): 290-307. http://www.researchgate.net/profile/Jia_Hu15/publication/316085316_Eco_approaching_at_an_isolated_signalized_intersection_under_partially_connected_and_automated_vehicles_environment/links/59da2716a6fdcc2aad129dca/Eco-approaching-at-an-isolated-signalized-intersection-under-partially-connected-and-automated-vehicles-environment.pdf [11] MALIKOPOULOS A A, CASSANDRA-S C G, ZHANG Y J, Adecentralized energyoptimal control framework for connected automated vehicles at signalfree intersections[J]. 2018(93): 244-256. [12] 袁娜, 史昕, 赵祥模. 车联网下道路交叉口车速引导信息管理系统[J]. 测控技术, 2019, 38(4): 142-147. https://www.cnki.com.cn/Article/CJFDTOTAL-IKJS201904031.htmYUAN Na, SHI Xin, ZHAO Xiangmo. Speed guidance information management system of road intersection based on internet of vehicles[J]. Measurement and Control Technology, 2019, 38(4): 142-147. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-IKJS201904031.htm [13] MA Fangwu, YANG Yu, WANG Jiawei, et al. Eco-drivingbased cooperative adaptive cruise control of connected vehicles platoon at signalized intersections[OL]. (2021-03)[2021-08-07]. https://www.sciencedirect.com/science/article/pii/S136192092100050X [14] 刘畅, 庄伟超, 殷国栋, 等. 高速匝道入口多智能网联车协同合流控制[J]. 东南大学学报(自然科学版), 2020, 50(5): 965-972. https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX202005024.htmLIU Chang, ZHUANG Weichao, YIN Guodong, et al. Cooperative merging control of multiple connected and automated vehicles on freeway ramp[J]. Joural of Southeast University(Natural Science Edition), 2020, 50(5): 965-972. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DNDX202005024.htm [15] 徐丽萍, 邓明君. 基于车路协同环境的信号交叉口车速引导方法[J]. 交通信息与安全, 2021, 39(2): 78-86. doi: 10.3963/j.jssn.1674-4861.2021.02.010XU Liping, DENG Mingjun. A speed guidance method at signalized intersections based on vehicle infrastructure cooperation[J]. Journal of Transport Information and Safety, 2021, 39(2): 78-86. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2021.02.010 [16] 郭戈, 许阳光, 徐涛, 等. 网联共享车路协同智能交通系统综述[J]. 控制与决策, 2019, 34(11): 2375-2389. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201911015.htmGUO Ge, XU Yangguang, XU Tao, et al. A survey of connected shared vehicle-road cooperative intelligent transportation systems[J]. Control and Decision, 2019, 34(11): 2375-2389. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201911015.htm [17] 缪立新, 王发平. V2X车联网关键技术研究及应用综述[J]. 汽车工程学报, 2020, 10(1): 1-12. doi: 10.3969/j.issn.2095-1469.2020.01.01MIAO Lixin, WANG Faping. Review on research and applications of V2X key technologies[J]. Chinese Journal of Automotive Engineering, 2020, 10(1): 1-12. (in Chinese) doi: 10.3969/j.issn.2095-1469.2020.01.01 [18] LIORIS J, PEDARSANI R, TASCIKARAOGLU F Y, et al. Platoons of connected vehicles can double throughput in urban roads[J]. Transportation Research Part C: Emerging Technologies, 2017(77): 292-305. [19] ZU Yue, LIU Chenhui, DAI Ran, et al. Real-time energy-efficient traffic control via convex optimization[J]. Transportation Research Part C: Emerging Technologies, 2018(92): 119-136. http://www.onacademic.com/detail/journal_1000040380638210_6c6c.html [20] LETTER C, ELEFTERIADOU L, Efficient control of fully automated connected vehicles at freeway merge segments[J]. Transportation Research Part C: Emerging Technologies, 2017(80): 190-205. http://www.sciencedirect.com/science/article/pii/S0968090X17301274 [21] United States Environmental Protection Agency. Motor vehicle emission simulator(MOVES): user guide for MOVES2014[OL]. (2014-07)[2021-08-07]. https://nepis.epa.gov/Exe/ZyPURL.cgi?Dockey=P100JWAL.txt. [22] ZEGEYE S K, SCHUTTER B D, HELLENDOORN J, et al. Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes[J]. Transportation Research Part C: Emerging Technologies, 2013, (31): 158-171. http://www.onacademic.com/detail/journal_1000035847778510_4246.html [23] PAJCHROWSKI T, ZAWIRSKI K, NOWOPOLSKI K. Neural speed controller trained online by means of modified RPROPalgorithm[J]. IEEE Transactions on Industrial Informatics, 2015, 11(2): 560-568. doi: 10.1109/TII.2014.2359620 -
点击查看大图
图(4) / 表(1)
计量
- 文章访问数: 536
- HTML全文浏览量: 197
- PDF下载量: 48
- 被引次数: 0