A Study on Autonomous Intersection Management: Planning-Based Strategy Improved by Convolutional Neural Network

The development and application of autonomous vehicles bring great changes to urban traffic management and control. As one of the bottlenecks to improve transportation efficiency, intersection management plays an important role in the urban city. When the dynamic control method in different cases is...

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Veröffentlicht in:	KSCE Journal of Civil Engineering Jg. 25; H. 10; S. 3995 - 4004
Hauptverfasser:	Zhang, Jian, Jiang, Xia, Liu, Ziyi, Zheng, Liang, Ran, Bin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Seoul Korean Society of Civil Engineers 01.10.2021 Springer Nature B.V 대한토목학회
Schlagworte:	Algorithms Artificial neural networks Civil Engineering Computation Computer simulation Control methods Dynamic control Engineering Geotechnical Engineering & Applied Earth Sciences Industrial Pollution Prevention Neural networks Strategy Time optimal control Traffic control Traffic intersections Traffic management Transportation Engineering Vehicle emissions Vehicles 토목공학 Planning based strategy Connected and automated vehicles Autonomous intersection Convolutional neural network
ISSN:	1226-7988, 1976-3808
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Abstract	The development and application of autonomous vehicles bring great changes to urban traffic management and control. As one of the bottlenecks to improve transportation efficiency, intersection management plays an important role in the urban city. When the dynamic control method in different cases is determined, the key of autonomous intersection management problem is to search the passing orders for approaching connected and automated vehicles (CAVs). The paper proposed a framework based on convolutional neural network to predict different passing orders’ total time consumption. Thus, the best passing order with the lowest time consume can be chosen as the optimal solution. Then continuous-time optimal control can be carried out on CAVs. Meanwhile, sequential model-based algorithm configuration technique is used for neural network training. Simulation results exported from Simulation of Urban Mobility (SUMO) indicate that the proposed method outperforms actuated signal control and first come first serve strategy. The average delay of the proposed method can decrease by 42.40%–73.05% compared with actuated signal control and 2.95%–55.29% compared to first come first serve strategy. Moreover, it can increase average speed by more than 20% compared with the other two methods. The proposed method can significantly reduce the computation time comparing with the original planning-based strategy. At last, the framework can be applied to other regression tasks like vehicle emissions, then different optimization targets can be estimated to get better solutions faster.
AbstractList	The development and application of autonomous vehicles bring great changes to urban traffic management and control. As one of the bottlenecks to improve transportation efficiency, intersection management plays an important role in the urban city. When the dynamic control method in different cases is determined, the key of autonomous intersection management problem is to search the passing orders for approaching connected and automated vehicles (CAVs). The paper proposed a framework based on convolutional neural network to predict different passing orders’ total time consumption. Thus, the best passing order with the lowest time consume can be chosen as the optimal solution. Then continuous-time optimal control can be carried out on CAVs. Meanwhile, sequential model-based algorithm configuration technique is used for neural network training. Simulation results exported from Simulation of Urban Mobility (SUMO) indicate that the proposed method outperforms actuated signal control and first come first serve strategy. The average delay of the proposed method can decrease by 42.40%–73.05% compared with actuated signal control and 2.95%–55.29% compared to first come first serve strategy. Moreover, it can increase average speed by more than 20% compared with the other two methods. The proposed method can significantly reduce the computation time comparing with the original planning-based strategy. At last, the framework can be applied to other regression tasks like vehicle emissions, then different optimization targets can be estimated to get better solutions faster. The development and application of autonomous vehicles bring great changes to urban traffic management and control. As one of the bottlenecks to improve transportation efficiency, intersection management plays an important role in the urban city. When the dynamic control method in different cases is determined, the key of autonomous intersection management problem is to search the passing orders for approaching connected and automated vehicles (CAVs). The paper proposed a framework based on convolutional neural network to predict different passing orders’ total time consumption. Thus, the best passing order with the lowest time consume can be chosen as the optimal solution. Then continuous-time optimal control can be carried out on CAVs. Meanwhile, sequential model-based algorithm configuration technique is used for neural network training. Simulation results exported from Simulation of Urban Mobility (SUMO) indicate that the proposed method outperforms actuated signal control and first come first serve strategy. The average delay of the proposed method can decrease by 42.40% − 73.05% compared with actuated signal control and 2.95% − 55.29% compared to first come first serve strategy. Moreover, it can increase average speed by more than 20% compared with the other two methods. The proposed method can significantly reduce the computation time comparing with the original planning-based strategy. At last, the framework can be applied to other regression tasks like vehicle emissions, then different optimization targets can be estimated to get better solutions faster. KCI Citation Count: 8
Author	Ran, Bin Liu, Ziyi Jiang, Xia Zhang, Jian Zheng, Liang
Author_xml	– sequence: 1 givenname: Jian orcidid: 0000-0002-9086-7622 surname: Zhang fullname: Zhang, Jian email: jianzhang@seu.edu.cn organization: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies and Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things, School of Transportation, Southeast University, School of Engineering, University of Tibet – sequence: 2 givenname: Xia orcidid: 0000-0002-8597-8520 surname: Jiang fullname: Jiang, Xia organization: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies and Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things, School of Transportation, Southeast University – sequence: 3 givenname: Ziyi orcidid: 0000-0001-9259-5811 surname: Liu fullname: Liu, Ziyi organization: Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies and Jiangsu Province Collaborative Innovation Center for Technology and Application of Internet of Things, School of Transportation, Southeast University – sequence: 4 givenname: Liang orcidid: 0000-0002-4161-9630 surname: Zheng fullname: Zheng, Liang organization: School of Traffic & Transportation Engineering, Central South University – sequence: 5 givenname: Bin orcidid: 0000-0002-5464-0930 surname: Ran fullname: Ran, Bin organization: Dept. of Civil and Environment Engineering, University of Wisconsin-Madison
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Cites_doi	10.1007/s12205-018-0528-2 10.1613/jair.2502 10.3141/2259-21 10.1080/15472450.2018.1519706 10.1007/s12205-018-1990-6 10.1016/j.trc.2019.05.003 10.1016/j.tra.2015.04.003 10.1109/TITS.2013.2277737 10.3141/2381-10 10.1016/j.tra.2016.05.013 10.1109/TVT.2019.2910987 10.1109/ACCESS.2019.2922227 10.1016/j.trc.2017.09.025 10.1016/j.neucom.2016.07.009 10.1016/j.ifacol.2016.07.007 10.1016/j.trb.2018.11.006 10.1016/j.patcog.2019.107131 10.1016/j.trb.2019.03.002 10.1109/TVT.2017.2780269 10.1109/TVT.2006.878730 10.1016/j.trc.2014.05.008 10.1080/15472450.2020.1742121 10.1109/ICAdLT.2013.6568462 10.1109/ITSC.2009.5309708 10.1007/978-3-642-25566-3_40 10.1109/ITSC.1999.821160 10.1109/IVS.2005.1505142 10.1145/1082473.1082545
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SubjectTerms	Algorithms Artificial neural networks Civil Engineering Computation Computer simulation Control methods Dynamic control Engineering Geotechnical Engineering & Applied Earth Sciences Industrial Pollution Prevention Neural networks Strategy Time optimal control Traffic control Traffic intersections Traffic management Transportation Engineering Vehicle emissions Vehicles 토목공학
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Title	A Study on Autonomous Intersection Management: Planning-Based Strategy Improved by Convolutional Neural Network
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