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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Zusammenfassung: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.
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ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-021-2093-3